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: "socially guided intrinsic motivation for robot learning of motor skills"
  Abstract: This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our algorithmic architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line, we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.

Connected through hyperedge HE-1 (induced by source paper "curiosity driven exploration of learned disentangled goal spaces"):
  with members:
    - "computational theories of curiosity driven learning"
    - "zero shot visual imitation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "hindsight experience replay"
    - "independently controllable factors"
    - "reverse curriculum generation for reinforcement learning"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"

Connected through hyperedge HE-2 (induced by source paper "computational theories of curiosity driven learning"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "policy search in continuous action domains an overview"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "hindsight experience replay"
    - "reverse curriculum generation for reinforcement learning"
    - "count based exploration with neural density models"
    - "the intentional unintentional agent learning to solve many continuous control tasks simultaneously"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"

Connected through hyperedge HE-3 (induced by source paper "accuracy based curriculum learning in deep reinforcement learning"):
  with members:
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "teacher student curriculum learning"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence and robotics"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a review of verbal and non verbal human robot interactive communication"

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)