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 "learning bidirectional lstm networks for synthesizing 3d mesh animation sequences"):
  Abstract: In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs). Synthesizing realistic 3D mesh animation sequences is a challenging and important task in computer animation. To achieve this, researchers have long been focusing on shape analysis to develop new interpolation and extrapolation techniques. However, such techniques have limited learning capabilities and therefore can produce unrealistic animation. Deep architectures that operate directly on mesh sequences remain unexplored, due to the following major barriers: meshes with irregular triangles, sequences containing rich temporal information and flexible deformations. To address these, we utilize convolutional neural networks defined on triangular meshes along with a shape deformation representation to extract useful features, followed by LSTM cells that iteratively process the features. To allow completion of a missing mesh sequence from given endpoints, we propose a new weight-shared bidirectional structure. The bidirectional generation loss also helps mitigate error accumulation over iterations. Benefiting from all these technical advances, our approach outperforms existing methods in sequence prediction and completion both qualitatively and quantitatively. Moreover, this network can also generate follow-up frames conditioned on initial shapes and improve the accuracy as more bootstrap models are provided, which other works in the geometry processing domain cannot achieve.

Includes members:
  Member "deep video generation prediction and completion of human action sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "sparse data driven mesh deformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rank3dgan semantic mesh generation using relative attributes")
  Member "variational autoencoders for deforming 3d mesh models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "deep representation learning for human motion prediction and classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "geometric deep learning going beyond euclidean data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "on human motion prediction using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "the pose knows video forecasting by generating pose futures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "auto conditioned recurrent networks for extended complex human motion synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")

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.GR(Graphics)