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: "combining recurrent neural networks and adversarial training for human motion synthesis and control"
  Abstract: This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method for training a RNNs model from prerecorded motion data. We implement recurrent neural networks with long short-term memory (LSTM) cells because they are capable of handling nonlinear dynamics and long term temporal dependencies present in human motions. Next, we train a refiner network using an adversarial loss, similar to Generative Adversarial Networks (GANs), such that the refined motion sequences are indistinguishable from real motion capture data using a discriminative network. We embed contact information into the generative deep learning model to further improve the performance of our generative model. The resulting model is appealing to motion synthesis and control because it is compact, contact-aware, and can generate an infinite number of naturally looking motions with infinite lengths. Our experiments show that motions generated by our deep learning model are always highly realistic and comparable to high-quality motion capture data. We demonstrate the power and effectiveness of our models by exploring a variety of applications, ranging from random motion synthesis, online/offline motion control, and motion filtering. We show the superiority of our generative model by comparison against baseline models.

Connected through hyperedge HE-1 (induced by source paper "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "a neural temporal model for human motion prediction"
    - "human motion modeling using dvgans"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-2 (induced by source paper "moglow probabilistic and controllable motion synthesis using normalising flows"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "analyzing input and output representations for speech driven gesture generation"
    - "generating diverse high fidelity images with vq vae 2"
    - "fixup initialization residual learning without normalization"
    - "videoflow a flow based generative model for video"
    - "waveglow a flow based generative network for speech synthesis"
    - "quaternet a quaternion based recurrent model for human motion"
    - "flowavenet a generative flow for raw audio"

Connected through hyperedge HE-3 (induced by source paper "learning diverse stochastic human action generators by learning smooth latent transitions"):
  with members:
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "deep video generation prediction and completion of human action sequences"
    - "human action generation with generative adversarial networks"
    - "stochastic adversarial video prediction"
    - "recurrent transition networks for character locomotion"
    - "dual motion gan for future flow embedded video prediction"
    - "hp gan probabilistic 3d human motion prediction via gan"

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.GR(Graphics)