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: "data driven approach to simulating realistic human joint constraints"
  Abstract: Modeling realistic human joint limits is important for applications involving physical human-robot interaction. However, setting appropriate human joint limits is challenging because it is pose-dependent: the range of joint motion varies depending on the positions of other bones. The paper introduces a new technique to accurately simulate human joint limits in physics simulation. We propose to learn an implicit equation to represent the boundary of valid human joint configurations from real human data. The function in the implicit equation is represented by a fully connected neural network whose gradients can be efficiently computed via back-propagation. Using gradients, we can efficiently enforce realistic human joint limits through constraint forces in a physics engine or as constraints in an optimization problem.

Connected through hyperedge HE-1 (induced by source paper "back to the future joint aware temporal deep learning 3d human pose estimation"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "deep high resolution representation learning for human pose estimation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "modeling human motion with quaternion based neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-2 (induced by source paper "assistive gym a physics simulation framework for assistive robotics"):
  with members:
    - "multidimensional capacitive sensing for robot assisted dressing and bathing"
    - "modeling collaboration for robot assisted dressing tasks"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "habitat a platform for embodied ai research"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "sim to real learning agile locomotion for quadruped robots"
    - "deep haptic model predictive control for robot assisted dressing"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-3 (induced by source paper "learning to collaborate from simulation for robot assisted dressing"):
  with members:
    - "assistive gym a physics simulation framework for assistive robotics"
    - "multidimensional capacitive sensing for robot assisted dressing and bathing"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "deep haptic model predictive control for robot assisted dressing"
    - "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

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.RO(Robotics)