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: "coupled cyclegan unsupervised hashing network for cross modal retrieval"
  Abstract: In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.

Connected through hyperedge HE-1 (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"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "zero shot sketch image hashing"
    - "evolutionary generative adversarial networks"
    - "masking a new perspective of noisy supervision"
    - "self supervised adversarial hashing networks for cross modal retrieval"

Connected through hyperedge HE-2 (induced by source paper "deep spectral clustering using dual autoencoder network"):
  with members:
    - "deep discriminative latent space for clustering"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep continuous clustering"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "online hashing with efficient updating of binary codes"):
  with members:
    - "towards optimal discrete online hashing with balanced similarity"
    - "mihash online hashing with mutual information"
    - "binary generative adversarial networks for image retrieval"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "microsoft coco common objects in context"
    - "the power of asymmetry in binary hashing"

Connected through hyperedge HE-4 (induced by source paper "efficient querying from weighted binary codes"):
  with members:
    - "towards optimal discrete online hashing with balanced similarity"

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.IR(Information Retrieval)