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: "a sensorimotor reinforcement learning framework for physical human robot interaction"
  Abstract: Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-efficient reinforcement learning framework which enables a robot to learn how to collaborate with a human partner. The robot learns the task from its own sensorimotor experiences in an unsupervised manner. The uncertainty of the human actions is modeled using Gaussian processes (GP) to implement action-value functions. Optimal action selection given the uncertain GP model is ensured by Bayesian optimization. We apply the framework to a scenario in which a human and a PR2 robot jointly control the ball position on a plank based on vision and force/torque data. Our experimental results show the suitability of the proposed method in terms of fast and data-efficient model learning, optimal action selection under uncertainties and equal role sharing between the partners.

Connected through hyperedge HE-1 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "riemannian motion policies"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "learning dynamic robot to human object handover from human feedback"

Connected through hyperedge HE-2 (induced by source paper "fast adaptation with meta reinforcement learning for trust modelling in human robot interaction"):
  with members:
    - "meta learning a survey"
    - "a comparison of visualisation methods for disambiguating verbal requests in human robot interaction"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "attention is all you need"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "one shot imitation learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "deep residual learning for image recognition"

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"
    - "mathematical models of adaptation in human robot collaboration"
    - "learning dynamic robot to human object handover from human feedback"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "robots that can adapt like animals"

Connected through hyperedge HE-4 (induced by source paper "neural network based reinforcement learning for audio visual gaze control in human robot interaction"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "deep predictive policy training using reinforcement learning"):
  with members:
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "self learning and adaptation in a sensorimotor framework"
    - "path integral guided policy search"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "deep spatial autoencoders for visuomotor learning"

Connected through hyperedge HE-6 (induced by source paper "human robot collaboration from psychology to social robotics"):
  with members:
    - "deep representation learning for human motion prediction and classification"
    - "anticipating many futures online human motion prediction and synthesis for human robot collaboration"
    - "deep predictive policy training using reinforcement learning"
    - "self learning and adaptation in a sensorimotor framework"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "end to end training of deep visuomotor policies"
    - "learning human activities and object affordances from rgb d videos"
    - "interaction histories and short term memory enactive development of turn taking behaviors in a childlike humanoid robot"

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)