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: "game theoretic modeling of human adaptation in human robot collaboration"
  Abstract: In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions, without replicating the robot's policy. We present a game-theoretic model of human partial adaptation to the robot, where the human responds to the robot's actions by maximizing a reward function that changes stochastically over time, capturing the evolution of their expectations of the robot's capabilities. The robot can then use this model to decide optimally between taking actions that reveal its capabilities to the human and taking the best action given the information that the human currently has. We prove that under certain observability assumptions, the optimal policy can be computed efficiently. We demonstrate through a human subject experiment that the proposed model significantly improves human-robot team performance, compared to policies that assume complete adaptation of the human to the robot.

Connected through hyperedge HE-1 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation"):
  with members:
    - "distilling policy distillation"
    - "learning novel policies for tasks"
    - "ranking based reward extrapolation without rankings"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "convergence of learning dynamics in stackelberg games"):
  with members:
    - "on gradient based learning in continuous games"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "goal inference improves objective and perceived performance in human robot collaboration"

Connected through hyperedge HE-3 (induced by source paper "building second order mental models for human robot interaction"):
  with members:
    - "on the utility of model learning in hri"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "modelling user s theory of ai s mind in interactive intelligent systems"
    - "machine theory of mind"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "towards efficient detection and optimal response against sophisticated opponents"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"

Connected through hyperedge HE-4 (induced by source paper "multi task trust transfer for human robot interaction"):
  with members:
    - "robot capability and intention in trust based decisions across tasks"
    - "establishing appropriate trust via critical states"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "human robot mutual adaptation in shared autonomy"
    - "generating sentences from a continuous space"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning to collaborate in markov decision processes"):
  with members:
    - "modeling others using oneself in multi agent reinforcement learning"
    - "an overview of machine teaching"
    - "online reinforcement learning in stochastic games"
    - "corralling a band of bandit algorithms"
    - "prediction with a short memory"
    - "fast convergence of regularized learning in games"
    - "online learning in markov decision processes with adversarially chosen transition probability distributions"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-6 (induced by source paper "embodiment in socially interactive robots"):
  with members:
    - "emotional storytelling using virtual and robotic agents"
    - "explorations in engagement for humans and robots"

Connected through hyperedge HE-7 (induced by source paper "human ai learning performance in multi armed bandits"):
  with members:
    - "expressing robot incapability"
    - "cooperative inverse reinforcement learning"
    - "modeling human decision making in generalized gaussian multi armed bandits"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "expressing robot incapability"):
  with members:
    - "communicating robot arm motion intent through mixed reality head mounted displays"
    - "expressive robot motion timing"
    - "intent communication between autonomous vehicles and pedestrians"
    - "exploring implicit human responses to robot mistakes in a learning from demonstration task"

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)