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: "shared predictive cross modal deep quantization"
  Abstract: With explosive growth of data volume and ever-increasing diversity of data modalities, cross-modal similarity search, which conducts nearest neighbor search across different modalities, has been attracting increasing interest. This paper presents a deep compact code learning solution for efficient cross-modal similarity search. Many recent studies have proven that quantization-based approaches perform generally better than hashing-based approaches on single-modal similarity search. In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search. Our approach, dubbed shared predictive deep quantization (SPDQ), explicitly formulates a shared subspace across different modalities and two private subspaces for individual modalities, and representations in the shared subspace and the private subspaces are learned simultaneously by embedding them to a reproducing kernel Hilbert space, where the mean embedding of different modality distributions can be explicitly compared. In addition, in the shared subspace, a quantizer is learned to produce the semantics preserving compact codes with the help of label alignment. Thanks to this novel network architecture in cooperation with supervised quantization training, SPDQ can preserve intramodal and intermodal similarities as much as possible and greatly reduce quantization error. Experiments on two popular benchmarks corroborate that our approach outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "multimodal unified attention networks for vision and language interactions"):
  with members:
    - "multimodal deep network embedding with integrated structure and attribute information"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-2 (induced by source paper "stacked semantic guided network for zero shot sketch based image retrieval"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "semantic autoencoder for zero shot learning"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "deep sketch hashing fast free hand sketch based image retrieval"

Connected through hyperedge HE-3 (induced by source paper "distillhash unsupervised deep hashing by distilling data pairs"):
  with members:
    - "fast supervised discrete hashing"
    - "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"
    - "self supervised adversarial hashing networks for cross modal retrieval"

Connected through hyperedge HE-4 (induced by source paper "semantic adversarial network with multi scale pyramid attention for video classification"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "learning long term dependencies for action recognition with a biologically inspired deep network"
    - "temporal convolutional networks for action segmentation and detection"

Connected through hyperedge HE-5 (induced by source paper "coupled cyclegan unsupervised hashing network for cross modal retrieval"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "triplet based deep hashing network for cross modal retrieval"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised generative adversarial cross modal hashing"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "hadamard matrix guided online hashing"):
  with members:
    - "fast supervised discrete hashing"
    - "towards optimal discrete online hashing with balanced similarity"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "simple and deterministic matrix sketching"

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)