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: "sch gan semi supervised cross modal hashing by generative adversarial network"
  Abstract: Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Supervised cross-modal hashing methods have achieved considerable progress by incorporating semantic side information. However, they mainly have two limitations: (1) Heavily rely on large-scale labeled cross-modal training data which are labor intensive and hard to obtain. (2) Ignore the rich information contained in the large amount of unlabeled data across different modalities, especially the margin examples that are easily to be incorrectly retrieved, which can help to model the correlations. To address these problems, in this paper we propose a novel Semi-supervised Cross-Modal Hashing approach by Generative Adversarial Network (SCH-GAN). We aim to take advantage of GAN's ability for modeling data distributions to promote cross-modal hashing learning in an adversarial way. The main contributions can be summarized as follows: (1) We propose a novel generative adversarial network for cross-modal hashing. In our proposed SCH-GAN, the generative model tries to select margin examples of one modality from unlabeled data when giving a query of another modality. While the discriminative model tries to distinguish the selected examples and true positive examples of the query. These two models play a minimax game so that the generative model can promote the hashing performance of discriminative model. (2) We propose a reinforcement learning based algorithm to drive the training of proposed SCH-GAN. The generative model takes the correlation score predicted by discriminative model as a reward, and tries to select the examples close to the margin to promote discriminative model by maximizing the margin between positive and negative data. Experiments on 3 widely-used datasets verify the effectiveness of our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "a semantics guided class imbalance learning model for zero shot classification"):
  with members:
    - "multi modal cycle consistent generalized zero shot learning"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "adaptive confidence smoothing for generalized zero shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "cmsalgan rgb d salient object detection with cross view generative adversarial networks"):
  with members:
    - "adaptive fusion for rgb d salient object detection"
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "a simple pooling based design for real time salient object detection"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "macro micro adversarial network for human parsing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "effective and efficient indexing in cross modal hashing based datasets"):
  with members:
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised generative adversarial cross modal hashing"
    - "discrete latent factor model for cross modal hashing"
    - "deep binary reconstruction for cross modal hashing"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "fast exact search in hamming space with multi index hashing"

Connected through hyperedge HE-5 (induced by source paper "label prediction framework for semi supervised cross modal retrieval"):
  with members:
    - "iterative learning with open set noisy labels"
    - "learning from noisy large scale datasets with minimal supervision"
    - "temporal ensembling for semi supervised learning"
    - "semi supervised deep learning by metric embedding"
    - "semi supervised learning with ladder 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.CV(Computer Vision and Pattern Recognition)