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: "bihmp gan bidirectional 3d human motion prediction gan"
  Abstract: Human motion prediction model has applications in various fields of computer vision. Without taking into account the inherent stochasticity in the prediction of future pose dynamics, such methods often converges to a deterministic undesired mean of multiple probable outcomes. Devoid of this, we propose a novel probabilistic generative approach called Bidirectional Human motion prediction GAN, or BiHMP-GAN. To be able to generate multiple probable human-pose sequences, conditioned on a given starting sequence, we introduce a random extrinsic factor r, drawn from a predefined prior distribution. Furthermore, to enforce a direct content loss on the predicted motion sequence and also to avoid mode-collapse, a novel bidirectional framework is incorporated by modifying the usual discriminator architecture. The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence. To further regularize the training, we introduce a novel recursive prediction strategy. In spite of being in a probabilistic framework, the enhanced discriminator architecture allows predictions of an intermediate part of pose sequence to be used as a conditioning for prediction of the latter part of the sequence. The bidirectional setup also provides a new direction to evaluate the prediction quality against a given test sequence. For a fair assessment of BiHMP-GAN, we report performance of the generated motion sequence using (i) a critic model trained to discriminate between real and fake motion sequence, and (ii) an action classifier trained on real human motion dynamics. Outcomes of both qualitative and quantitative evaluations, on the probabilistic generations of the model, demonstrate the superiority of BiHMP-GAN over previously available methods.

Connected through hyperedge HE-1 (induced by source paper "sampling good latent variables via cpp vaes vaes with condition posterior as prior"):
  with members:
    - "a surprisingly effective fix for deep latent variable modeling of text"
    - "implicit deep latent variable models for text generation"
    - "learning variations in human motion via mix and match perturbation"
    - "mixture content selection for diverse sequence generation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "mixture models for diverse machine translation tricks of the trade"
    - "diversity sensitive conditional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "context aware human motion prediction"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "an lstm network for highway trajectory prediction"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "graph based global reasoning networks"

Connected through hyperedge HE-3 (induced by source paper "learning variations in human motion via mix and match perturbation"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "videoflow a flow based generative model for video"
    - "modeling human motion with quaternion based neural networks"
    - "human motion modeling using dvgans"
    - "quaternet a quaternion based recurrent model for human motion"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "viena2 a driving anticipation dataset"
    - "a variational u net for conditional appearance and shape generation"

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)