human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "modality to modality translation an adversarial representation learning and graph fusion network for multimodal fusion"):
  Abstract: Learning joint embedding space for various modalities is of vital importance for multimodal fusion. Mainstream modality fusion approaches fail to achieve this goal, leaving a modality gap which heavily affects cross-modal fusion. In this paper, we propose a novel adversarial encoder-decoder-classifier framework to learn a modality-invariant embedding space. Since the distributions of various modalities vary in nature, to reduce the modality gap, we translate the distributions of source modalities into that of target modality via their respective encoders using adversarial training. Furthermore, we exert additional constraints on embedding space by introducing reconstruction loss and classification loss. Then we fuse the encoded representations using hierarchical graph neural network which explicitly explores unimodal, bimodal and trimodal interactions in multi-stage. Our method achieves state-of-the-art performance on multiple datasets. Visualization of the learned embeddings suggests that the joint embedding space learned by our method is discriminative.

Includes members:
  Member "strong and simple baselines for multimodal utterance embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on emergent communication in competitive multi agent teams")
  Member "multimodal transformer for unaligned multimodal language sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stroke constrained attention network for online handwritten mathematical expression recognition")
  Member "multimodal sentiment analysis using hierarchical fusion with context modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio text sentiment analysis using deep robust complementary fusion of multi features and multi modalities")
  Member "multimodal language analysis with recurrent multistage fusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on emergent communication in competitive multi agent teams")
  Member "multi attention recurrent network for human communication comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis")
  Member "investigating audio visual and text fusion methods for end to end automatic personality prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent trends in deep learning based personality detection")
  Member "multimodal affective analysis using hierarchical attention strategy with word level alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross modal learning for multi modal video categorization")
  Member "found in translation learning robust joint representations by cyclic translations between modalities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding")

Question: Please tell me which class the source paper 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)