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: "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"
  Abstract: Cross-modal hashing is usually regarded as an effective technique for large-scale textual-visual cross retrieval, where data from different modalities are mapped into a shared Hamming space for matching. Most of the traditional textual-visual binary encoding methods only consider holistic image representations and fail to model descriptive sentences. This renders existing methods inappropriate to handle the rich semantics of informative cross-modal data for quality textual-visual search tasks. To address the problem of hashing cross-modal data with semantic-rich cues, in this paper, a novel integrated deep architecture is developed to effectively encode the detailed semantics of informative images and long descriptive sentences, named as Textual-Visual Deep Binaries (TVDB). In particular, region-based convolutional networks with long short-term memory units are introduced to fully explore image regional details while semantic cues of sentences are modeled by a text convolutional network. Additionally, we propose a stochastic batch-wise training routine, where high-quality binary codes and deep encoding functions are efficiently optimized in an alternating manner. Experiments are conducted on three multimedia datasets, i.e. Microsoft COCO, IAPR TC-12, and INRIA Web Queries, where the proposed TVDB model significantly outperforms state-of-the-art binary coding methods in the task of cross-modal retrieval.

Connected through hyperedge HE-1 (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-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"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"

Connected through hyperedge HE-3 (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"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-4 (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"
    - "asymmetric deep supervised hashing"
    - "hashnet deep learning to hash by continuation"
    - "supervised deep hashing for hierarchical labeled data"

Connected through hyperedge HE-5 (induced by source paper "zero shot sketch image hashing"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "stochastic generative hashing"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "self supervised adversarial hashing networks for cross modal retrieval"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "deep cross modal hashing"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "a survey on multi view learning"
    - "representation learning a review and new perspectives"
    - "multimodal similarity preserving 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)