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: "intrinsically motivated reinforcement learning for human robot interaction in the real world"
  Abstract: Abstract   For a natural social human–robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human–robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement.

Connected through hyperedge HE-1 (induced by source paper "composing ensembles of policies with deep reinforcement learning"):
  with members:
    - "data efficient hierarchical reinforcement learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to compose skills"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"

Connected through hyperedge HE-2 (induced by source paper "composing task agnostic policies with deep reinforcement learning"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "composable deep reinforcement learning for robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "learning to compose skills"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "stochastic neural networks for hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "adversarial imitation via variational inverse reinforcement learning"):
  with members:
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "robot autonomy for surgery"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "stepwise acquisition of dialogue act through human robot interaction"):
  with members:
    - "sequential short text classification with recurrent and convolutional neural networks"

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)