human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "multi scale orderless pooling of deep convolutional activation features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "visualizing and understanding convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep inside convolutional networks visualising image classification models and saliency maps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "network in network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "multi column deep neural networks for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "importance driven deep learning system testing")

Question: Please tell me which class the source paper 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)