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: "mathematical models of adaptation in human robot collaboration"
  Abstract: A robot operating in isolation needs to reason over the uncertainty in its model of the world and adapt its own actions to account for this uncertainty. Similarly, a robot interacting with people needs to reason over its uncertainty over the human internal state, as well as over how this state may change, as humans adapt to the robot. This paper summarizes our own work in this area, which depicts the different ways that probabilistic planning and game-theoretic algorithms can enable such reasoning in robotic systems that collaborate with people. We start with a general formulation of the problem as a two-player game with incomplete information. We then articulate the different assumptions within this general formulation, and we explain how these lead to exciting and diverse robot behaviors in real-time interactions with actual human subjects, in a variety of manufacturing, personal robotics and assistive care settings.

Connected through hyperedge HE-1 (induced by source paper "learning to switch between machines and humans"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "regression under human assistance"
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "selectivenet a deep neural network with an integrated reject option"
    - "the algorithmic automation problem prediction triage and human effort"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "direct uncertainty prediction for medical second opinions"

Connected through hyperedge HE-2 (induced by source paper "towards deployment of robust ai agents for human machine partnerships"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "learning to collaborate in markov decision processes"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "learning policy representations in multiagent systems"
    - "robust adversarial reinforcement learning"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-3 (induced by source paper "interactive trajectory adaptation through force guided bayesian optimization"):
  with members:
    - "trajectory deformations from physical human robot interaction"
    - "deep kernels for optimizing locomotion controllers"
    - "learning dynamic robot to human object handover from human feedback"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "robots that can adapt like animals"

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)