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: "modeling collaboration for robot assisted dressing tasks"
  Abstract: We investigated the application of haptic aware feedback control and deep reinforcement learning to robot assisted dressing in simulation. We did so by modeling both human and robot control policies as separate neural networks and training them both via TRPO. We show that co-optimization, training separate human and robot control policies simultaneously, can be a valid approach to finding successful strategies for human/robot cooperation on assisted dressing tasks. Typical tasks are putting on one or both sleeves of a hospital gown or pulling on a T-shirt. We also present a method for modeling human dressing behavior under variations in capability including: unilateral muscle weakness, Dyskinesia, and limited range of motion. Using this method and behavior model, we demonstrate discovery of successful strategies for a robot to assist humans with a variety of capability limitations.

Connected through hyperedge HE-1 (induced by source paper "assistive gym a physics simulation framework for assistive robotics"):
  with members:
    - "multidimensional capacitive sensing for robot assisted dressing and bathing"
    - "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"
    - "task centric optimization of configurations for assistive robots"

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)