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: "robotic playing for hierarchical complex skill learning"
  Abstract: In complex manipulation scenarios (e.g. tasks requiring complex interaction of two hands or in-hand manipulation), generalization is a hard problem. Current methods still either require a substantial amount of (supervised) training data and / or strong assumptions on both the environment and the task. In this paradigm, controllers solving these tasks tend to be complex. We propose a paradigm of maintaining simpler controllers solving the task in a small number of specific situations. In order to generalize to novel situations, the robot transforms the environment from novel situations into a situation where the solution of the task is already known. Our solution to this problem is to play with objects and use previously trained skills (basis skills). These skills can either be used for estimating or for changing the current state of the environment and are organized in skill hierarchies. The approach is evaluated in complex pick-and-place scenarios that involve complex manipulation. We further show that these skills can be learned by autonomous playing.

Connected through hyperedge HE-1 (induced by source paper "self supervised damage avoiding manipulation strategy optimization via mental simulation"):
  with members:
    - "kittingbot a mobile manipulation robot for collaborative kitting in automotive logistics"
    - "physics based damage aware manipulation strategy planning using scene dynamics anticipation"
    - "knowledge enabled robotic agents for shelf replenishment in cluttered retail environments"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "what happens if learning to predict the effect of forces in images"

Connected through hyperedge HE-2 (induced by source paper "how a minimal learning agent can infer the existence of unobserved variables in a complex environment"):
  with members:
    - "benchmarking projective simulation in navigation problems"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "projective simulation with generalization"

Connected through hyperedge HE-3 (induced by source paper "benchmarking projective simulation in navigation problems"):
  with members:
    - "projective simulation with generalization"
    - "skill learning by autonomous robotic playing using active learning and creativity"
    - "meta learning within projective simulation"
    - "an alternative softmax operator for reinforcement learning"
    - "learning to navigate in complex environments"
    - "projective simulation applied to the grid world and the mountain car problem"

Connected through hyperedge HE-4 (induced by source paper "projective simulation with generalization"):
  with members:
    - "meta learning within projective simulation"
    - "projective simulation applied to the grid world and the mountain car problem"
    - "feature reinforcement learning in practice"
    - "feature reinforcement learning part i unstructured mdps"
    - "bandit problems with side observations"

Connected through hyperedge HE-5 (induced by source paper "skill learning by autonomous robotic playing using active learning and creativity"):
  with members:
    - "projective simulation with generalization"
    - "projective simulation applied to the grid world and the mountain car problem"
    - "active learning with statistical models"

Connected through hyperedge HE-6 (induced by source paper "a novel skill based programming paradigm based on autonomous playing and skill centric testing"):
  with members:
    - "skill learning by autonomous robotic playing using active learning and creativity"
    - "newton methods for k order markov constrained motion problems"

Connected through hyperedge HE-7 (induced by source paper "self supervised damage avoiding manipulation strategy optimization via mental simulation"):
  with members:
    - "physics based damage aware manipulation strategy planning using scene dynamics anticipation"

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)