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: "self learning and adaptation in a sensorimotor framework"
  Abstract: We present a general framework to autonomously achieve a task, where autonomy is acquired by learning sensorimotor patterns of a robot, while it is interacting with its environment. To accomplish the task, using the learned sensorimotor contingencies, our approach predicts a sequence of actions that will lead to the desirable observations. Gaussian processes (GP) with automatic relevance determination is used to learn the sensorimotor mapping. In this way, relevant sensory and motor components can be systematically found in high-dimensional sensory and motor spaces. We propose an incremental GP learning strategy, which discerns between situations, when an update or an adaptation must be implemented. RRT* is exploited to enable long-term planning and generating a sequence of states that lead to a given goal; while a gradient-based search finds the optimum action to steer to a neighbouring state in a single time step. Our experimental results prove the successfulness of the proposed framework to learn a joint space controller with high data dimensions (10$\times$15). It demonstrates short training phase (less than 12 seconds), real-time performance and rapid adaptations capabilities.

Connected through hyperedge HE-1 (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"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "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-2 (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"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "grounding the experience of a visual field through sensorimotor contingencies"):
  with members:
    - "grounding perception a developmental approach to sensorimotor contingencies"
    - "autonomous grounding of visual field experience through sensorimotor prediction"
    - "grounding object perception in a naive agent s sensorimotor experience"
    - "learning agent s spatial configuration from sensorimotor invariants"
    - "space as an invention of biological organisms"
    - "representation learning a review and new perspectives"

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)