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: "subic a supervised structured binary code for image search"
  Abstract: For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the supervision, end-to-end learning and novel architectures ushered in by the deep learning revolution. We hence propose herein a novel method to make deep convolutional neural networks produce supervised, compact, structured binary codes for visual search. Our method makes use of a novel block-softmax non-linearity and of batch-based entropy losses that together induce structure in the learned encodings. We show that our method outperforms state-of-the-art compact representations based on deep hashing or structured quantization in single and cross-domain category retrieval, instance retrieval and classification. We make our code and models publicly available online.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep hashing methods"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "deep triplet quantization"
    - "hadamard codebook based deep hashing"
    - "central similarity hashing via hadamard matrix"
    - "deep attention guided hashing"
    - "deep spherical quantization for image search"
    - "deep ordinal hashing with spatial attention"

Connected through hyperedge HE-2 (induced by source paper "generalized product quantization network for semi supervised image retrieval"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "deep triplet quantization"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "a closer look at few shot classification"
    - "open set domain adaptation by backpropagation"
    - "prototypical networks for few shot learning"
    - "understanding deep learning requires rethinking generalization"
    - "scalable image retrieval by sparse product quantization"

Connected through hyperedge HE-3 (induced by source paper "data free learning of student networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "interpretable convolutional neural networks"
    - "data free knowledge distillation for deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "embarrassingly simple binary representation learning"):
  with members:
    - "zero shot sketch image hashing"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "mihash online hashing with mutual information"
    - "binary generative adversarial networks for image retrieval"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"

Connected through hyperedge HE-5 (induced by source paper "unsupervised neural quantization for compressed domain similarity search"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "in defense of product quantization"
    - "super convergence very fast training of residual networks using large learning rates"

Connected through hyperedge HE-6 (induced by source paper "quicker adc unlocking the hidden potential of product quantization with simd"):
  with members:
    - "derived codebooks for high accuracy nearest neighbor search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "faster population counts using avx2 instructions"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "billion scale similarity search with gpus"
    - "cuckoo hash tables high performance hash tables for networking applications"
    - "in defense of product quantization"

Connected through hyperedge HE-7 (induced by source paper "deep spherical quantization for image search"):
  with members:
    - "deep triplet quantization"
    - "cosface large margin cosine loss for deep face recognition"
    - "triplet center loss for multi view 3d object retrieval"
    - "deep supervised discrete hashing"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "how should we evaluate supervised hashing"

Connected through hyperedge HE-8 (induced by source paper "deep triplet quantization"):
  with members:
    - "hashnet deep learning to hash by continuation"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "optimized cartesian k means"

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)