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: "stochastic generative hashing"
  Abstract: Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases. However, due to the requirement of discrete outputs for the hash functions, learning such functions is known to be very challenging. In addition, the objective functions adopted by existing hashing techniques are mostly chosen heuristically. In this paper, we propose a novel generative approach to learn hash functions through Minimum Description Length principle such that the learned hash codes maximally compress the dataset and can also be used to regenerate the inputs. We also develop an efficient learning algorithm based on the stochastic distributional gradient, which avoids the notorious difficulty caused by binary output constraints, to jointly optimize the parameters of the hash function and the associated generative model. Extensive experiments on a variety of large-scale datasets show that the proposed method achieves better retrieval results than the existing state-of-the-art methods.

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 "auto encoding twin bottleneck hashing"):
  with members:
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "embarrassingly simple binary representation learning"
    - "zero shot sketch image hashing"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "hashnet deep learning to hash by continuation"
    - "semi supervised classification with graph convolutional networks"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-3 (induced by source paper "cycle consistent deep generative hashing for cross modal retrieval"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "deep semantic hashing with generative adversarial networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "zero shot sketch image hashing"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "cycada cycle consistent adversarial domain adaptation"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Connected through hyperedge HE-4 (induced by source paper "learning compressed sentence representations for on device text processing"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "improving sentence representations with multi view frameworks"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "near lossless binarization of word embeddings"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-5 (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"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-6 (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"
    - "subic a supervised structured binary code for image search"
    - "mihash online hashing with mutual information"
    - "binary generative adversarial networks for image retrieval"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "hashnet deep learning to hash by continuation"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"

Connected through hyperedge HE-7 (induced by source paper "distillhash unsupervised deep hashing by distilling data pairs"):
  with members:
    - "fast supervised discrete hashing"
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "zero shot sketch image hashing"
    - "evolutionary generative adversarial networks"
    - "masking a new perspective of noisy supervision"

Connected through hyperedge HE-8 (induced by source paper "new loss functions for fast maximum inner product search"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "trained ternary quantization"
    - "practical and optimal lsh for angular distance"
    - "quantization based fast inner product search"
    - "hashing for similarity search a survey"
    - "fast exact search in hamming space with multi index 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.LG(Machine Learning)