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: "cm gans cross modal generative adversarial networks for common representation learning"
  Abstract: It is known that the inconsistent distribution and representation of different modalities, such as image and text, cause the heterogeneity gap that makes it challenging to correlate such heterogeneous data. Generative adversarial networks (GANs) have shown its strong ability of modeling data distribution and learning discriminative representation, existing GANs-based works mainly focus on generative problem to generate new data. We have different goal, aim to correlate heterogeneous data, by utilizing the power of GANs to model cross-modal joint distribution. Thus, we propose Cross-modal GANs to learn discriminative common representation for bridging heterogeneity gap. The main contributions are: (1) Cross-modal GANs architecture is proposed to model joint distribution over data of different modalities. The inter-modality and intra-modality correlation can be explored simultaneously in generative and discriminative models. Both of them beat each other to promote cross-modal correlation learning. (2) Cross-modal convolutional autoencoders with weight-sharing constraint are proposed to form generative model. They can not only exploit cross-modal correlation for learning common representation, but also preserve reconstruction information for capturing semantic consistency within each modality. (3) Cross-modal adversarial mechanism is proposed, which utilizes two kinds of discriminative models to simultaneously conduct intra-modality and inter-modality discrimination. They can mutually boost to make common representation more discriminative by adversarial training process. To the best of our knowledge, our proposed CM-GANs approach is the first to utilize GANs to perform cross-modal common representation learning. Experiments are conducted to verify the performance of our proposed approach on cross-modal retrieval paradigm, compared with 10 methods on 3 cross-modal datasets.

Connected through hyperedge HE-1 (induced by source paper "learning visual relation priors for image text matching and image captioning with neural scene graph generators"):
  with members:
    - "relational reasoning using prior knowledge for visual captioning"
    - "rethinking visual relationships for high level image understanding"
    - "object driven text to image synthesis via adversarial training"
    - "vrr vg refocusing visually relevant relationships"
    - "scene graph reasoning with prior visual relationship for visual question answering"
    - "stacked cross attention for image text matching"
    - "multi modal learning with prior visual relation reasoning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-2 (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"
    - "cycada cycle consistent adversarial domain adaptation"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "hashgan attention aware deep adversarial hashing for cross modal retrieval"

Connected through hyperedge HE-3 (induced by source paper "learning cross modal embeddings with adversarial networks for cooking recipes and food images"):
  with members:
    - "a survey on food computing"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "can a chatbot determine my diet addressing challenges of chatbot application for meal recommendation"
    - "in defense of the triplet loss for person re identification"
    - "cycada cycle consistent adversarial domain adaptation"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"

Connected through hyperedge HE-4 (induced by source paper "learning to clean a gan perspective"):
  with members:
    - "pm gans discriminative representation learning for action recognition using partial modalities"
    - "marta gans unsupervised representation learning for remote sensing image classification"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "image to image translation with conditional adversarial networks"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "adversarial cross modal retrieval via learning and transferring single modal similarities"):
  with members:
    - "mhtn modal adversarial hybrid transfer network for cross modal retrieval"
    - "a multi view embedding space for modeling internet images tags and their semantics"

Connected through hyperedge HE-6 (induced by source paper "stacked cross attention for image text matching"):
  with members:
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "dual path convolutional image text embedding with instance loss"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-7 (induced by source paper "pm gans discriminative representation learning for action recognition using partial modalities"):
  with members:
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "adversarial discriminative domain adaptation"
    - "rank pooling for action recognition"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-8 (induced by source paper "dropout gan learning from a dynamic ensemble of discriminators"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "on convergence and stability of gans"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "triangle generative adversarial networks"
    - "the numerics of gans"
    - "multi generator generative adversarial nets"
    - "boosted generative models"

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.MM(Multimedia)