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: "fast exact search in hamming space with multi index hashing"
  Abstract: There is growing interest in representing image data and feature descriptors using compact binary codes for fast near neighbor search. Although binary codes are motivated by their use as direct indices (addresses) into a hash table, codes longer than 32 bits are not being used as such, as it was thought to be ineffective. We introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact k-nearest neighbor search in Hamming space. The approach is storage efficient and straightforward to implement. Theoretical analysis shows that the algorithm exhibits sub-linear run-time behavior for uniformly distributed codes. Empirical results show dramatic speedups over a linear scan baseline for datasets of up to one billion codes of 64, 128, or 256 bits.

Connected through hyperedge HE-1 (induced by source paper "deep multi index hashing for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "deep supervised discrete hashing"
    - "in defense of the triplet loss for person re identification"
    - "a revisit on deep hashings for large scale content based image retrieval"
    - "re ranking person re identification with k reciprocal encoding"
    - "stochastic generative hashing"

Connected through hyperedge HE-2 (induced by source paper "deep attention guided hashing"):
  with members:
    - "deep priority hashing"
    - "deep saliency hashing"
    - "deep supervised discrete hashing"
    - "deep binary reconstruction for cross modal hashing"
    - "hashnet deep learning to hash by continuation"
    - "pose invariant embedding for deep person re identification"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-3 (induced by source paper "candidate generation with binary codes for large scale top n recommendation"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "discrete factorization machines for fast feature based recommendation"
    - "deep discrete supervised hashing"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "the case for learned index structures"
    - "neural collaborative filtering"
    - "translation based recommendation"

Connected through hyperedge HE-4 (induced by source paper "asymmetric deep semantic quantization for image retrieval"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "deep attention guided hashing"
    - "deep ordinal hashing with spatial attention"
    - "deep priority hashing"
    - "deep discrete supervised hashing"
    - "deep supervised discrete hashing"
    - "asymmetric deep supervised hashing"
    - "a survey on learning to hash"

Connected through hyperedge HE-5 (induced by source paper "hamming sentence embeddings for information retrieval"):
  with members:
    - "word2bits quantized word vectors"
    - "near lossless binarization of word embeddings"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "compressing word embeddings via deep compositional code learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (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"
    - "subic a supervised structured binary code for image search"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "binary stochastic representations for large multi class classification"):
  with members:
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "lshtc a benchmark for large scale text classification"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-8 (induced by source paper "effective and efficient indexing in cross modal hashing based datasets"):
  with members:
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised generative adversarial cross modal hashing"
    - "discrete latent factor model for cross modal hashing"
    - "deep binary reconstruction for cross modal hashing"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"

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)