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: "group invariant deep representations for image instance retrieval"
  Abstract: Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors for image instance retrieval. While CNN-based descriptors are generally remarked for good retrieval performance at lower bitrates, they nevertheless present a number of drawbacks including the lack of robustness to common object transformations such as rotations compared with their interest point based FV counterparts. #R##N#In this paper, we propose a method for computing invariant global descriptors from CNNs. Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network. The resulting global descriptors can be made invariant to multiple arbitrary transformation groups while retaining good discriminativeness. #R##N#Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated. We also show that our method which has few parameters is not prone to overfitting: improvements generalize well across datasets with different properties with regard to invariances. Finally, we show that our descriptors are able to compare favourably to other state-of-the-art compact descriptors in similar bitranges, exceeding the highest retrieval results reported in the literature on some datasets. A dedicated dimensionality reduction step --quantization or hashing-- may be able to further improve the competitiveness of the descriptors.

Connected through hyperedge HE-1 (induced by source paper "invariant tensor feature coding"):
  with members:
    - "monet moments embedding network"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "harmonic networks deep translation and rotation equivariance"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "supervised learning of semantics preserving hash via deep convolutional neural networks"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "deep residual learning for image recognition"
    - "exploiting local features from deep networks for image retrieval"
    - "aggregating deep convolutional features for image retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "compression of deep neural networks for image instance retrieval"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nested invariance pooling and rbm hashing for image instance retrieval"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"

Connected through hyperedge HE-4 (induced by source paper "pruning convolutional neural networks for image instance retrieval"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "pruning convolutional neural networks for resource efficient inference"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "tiny descriptors for image retrieval with unsupervised triplet hashing"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"

Connected through hyperedge HE-5 (induced by source paper "sift meets cnn a decade survey of instance retrieval"):
  with members:
    - "polysemous codes"
    - "faster r cnn features for instance search"
    - "lift learned invariant feature transform"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "good practice in cnn feature transfer"
    - "a survey on learning to hash"

Connected through hyperedge HE-6 (induced by source paper "local feature detectors descriptors and image representations a survey"):
  with members:
    - "lift learned invariant feature transform"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tilde a temporally invariant learned detector"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "exploiting local features from deep networks for image retrieval"
    - "learning to compare image patches via convolutional neural networks"

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)