human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "scale invariant convolutional neural networks"
  Abstract: Abstract Even though convolutional neural networks (CNN) hasachieved near-human performance in various computer vi-sion tasks, its ability to tolerate scale variations is lim-ited. The popular practise is making the model biggerﬁrst, and then train it with data augmentation using ex-tensive scale-jittering. In this paper, we propose a scale-invariant convolutional neural network (SiCNN), a modeldesigned to incorporate multi-scale feature exaction andclassiﬁcation into the network structure. SiCNN uses amulti-column architecture, with each column focusing on aparticular scale. Unlike previous multi-column strategies,these columns share the same set of ﬁlter parameters by ascale transformation among them. This design deals withscale variation without blowing up the model size. Experi-mental results show that SiCNN detects features at variousscales, and the classiﬁcation result exhibits strong robust-ness against object scale variations. 1. Introduction Many classical computer vision tasks have enjoyed agreat breakthrough, primarily due to the large amount oftraining data and the application of deep convolution neu-ral networks (CNN) [8]. In the most recent ILSVRC 2014competition [11], CNN-based solutions have achieved near-human accuracies in image classiﬁcation, localization anddetection tasks [14,16].Accompanying this progress are studies trying to under-stand what CNN has learnt internally and what contributeto its success [2,13,17]. By design, layers within the net-work have progressively larger receptive ﬁeld sizes, allow-ing them to learn more complex features. Another key pointis the shift-invariance property, that a pattern in the inputcan be recognized regardless of its position [9]. Pooling lay-ers contribute resilience to slight deformation as well smallscale change [12].However, it is evident that CNN deals with shift-variancefar better than scale-invariance [3]. Not dealing with scale-invariance well poses a direct conﬂict to the design philos-ophy of CNN, in that higher layers may see and thus cap-tures features of certain plain patterns simply because theyare larger at the input, not because they are more complex.In other words, there is no alignments between in the posi-tion of a ﬁlter and the complexity it captures. What is more,there are other invariance that CNN does not deal with inter-nally. Examples include rotations and ﬂips (since featuresof natural objects are mostly symmetric).A brutal force solution would be to make the networklarger by introducing more ﬁlters to cope with scale vari-ations of the same feature, accompanied by scale-jitteringthe input images, often by order of magnitude. This is, infact, the popular practice today [1,8,14]. It is true even forproposals that directly deal with this problem. For example,[3] drives the CNN with crops of different size and positionswith three differnt scales, and then uses VLAD pooling toproduce a feature summary of the patches.We explore a radically different approach that is alsosimple. Observing that ﬁlters that detect the same patternbut with different scales bear strong relationship, we adopt amulti-column design and designate each column to special-ize on certain scales. We call our system SiCNN (Scale-invariant CNN). Unlike a conventional multi-column CNN,ﬁlters in SiCNN are strongly regulated among columns.The goal is to make the network resilient to scale variancewithout blowing up number of free parameters, and thus re-duce the need of jittering the input.We performed detailed analysis and veriﬁed that SiCNNexhibits the desired behavior. For example, the column thatdeals with larger scale is indeed activated by input patternswith the larger scaling factor, and the system as a wholebecomes less sensitive to scale variance. On unaugmentedCIFAR-10 dataset [7], our method produces the best re-1

Connected through hyperedge HE-1 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "collaborative deep reinforcement learning for joint object search"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "dct conv coding filters in convolutional networks with discrete cosine transform"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "leukonet dct based cnn architecture for the classification of normal versus leukemic blasts in b all cancer"
    - "rotation equivariant vector field networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "deep high resolution representation learning for human pose estimation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-4 (induced by source paper "mix match training convnets with mixed image sizes for improved accuracy speed and scale resiliency"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "faster neural network training with data echoing"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "yolov3 an incremental improvement"
    - "regularized evolution for image classifier architecture search"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-5 (induced by source paper "towards learning affine invariant representations via data efficient cnns"):
  with members:
    - "gabor convolutional networks"
    - "differentiable mpc for end to end planning and control"
    - "dynamic filtering with large sampling field for convnets"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-6 (induced by source paper "scale wise convolution for image restoration"):
  with members:
    - "non local recurrent network for image restoration"
    - "wide activation for efficient and accurate image super resolution"
    - "sniper efficient multi scale training"
    - "image super resolution via dual state recurrent networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "dssd deconvolutional single shot detector"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-7 (induced by source paper "scale equivariant steerable networks"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "scale steerable filters for locally scale invariant convolutional neural networks"
    - "harmonic networks with limited training samples"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant transformer networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

Connected through hyperedge HE-8 (induced by source paper "temporal transformer networks joint learning of invariant and discriminative time warping"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "can recurrent neural networks warp time"
    - "learning to exploit invariances in clinical time series data using sequence transformer networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "dynamic routing between capsules"
    - "learning invariant riemannian geometric representations using deep nets"
    - "graph based isometry invariant representation learning"
    - "a new representation of skeleton sequences for 3d action recognition"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)