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: "asymmetric deep supervised hashing"
  Abstract: Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points. The training of these symmetric deep supervised hashing methods is typically time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale database. In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search. ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned. The training of ADSH is much more efficient than that of traditional symmetric deep supervised hashing methods. Experiments show that ADSH can achieve state-of-the-art performance in real applications.

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 "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-3 (induced by source paper "deep supervised hashing leveraging quadratic spherical mutual information for content based image retrieval"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "mihash online hashing with mutual information"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep supervised hashing with triplet labels"
    - "densely connected convolutional networks"
    - "a survey on learning to hash"

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"
    - "a survey on learning to hash"
    - "deep cross modal hashing"

Connected through hyperedge HE-5 (induced by source paper "deep cross modal hashing with hashing functions and unified hash codes jointly learning"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "deep semantic hashing with generative adversarial networks"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised triplet hashing for fast image retrieval"
    - "discrete latent factor model for cross modal hashing"
    - "hashnet deep learning to hash by continuation"
    - "supervised deep hashing for hierarchical labeled data"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"

Connected through hyperedge HE-6 (induced by source paper "supervised online hashing via hadamard codebook learning"):
  with members:
    - "mihash online hashing with mutual information"
    - "ordinal constrained binary code learning for nearest neighbor search"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "simple and deterministic matrix sketching"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-7 (induced by source paper "towards optimal discrete online hashing with balanced similarity"):
  with members:
    - "mihash online hashing with mutual information"
    - "a survey on learning to hash"
    - "learning to hash for indexing big data a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-8 (induced by source paper "neurons merging layer towards progressive redundancy reduction for deep supervised hashing"):
  with members:
    - "zero shot sketch image hashing"
    - "deep discrete supervised hashing"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "hashing with binary matrix pursuit"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "deep supervised discrete hashing"
    - "nisp pruning networks using neuron importance score propagation"
    - "foresthash semantic hashing with shallow random forests and tiny convolutional 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.LG(Machine Learning)