human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "acquiring target stacking skills by goal parameterized deep reinforcement learning"):
  Abstract: Understanding physical phenomena is a key component of human intelligence and enables physical interaction with previously unseen environments. In this paper, we study how an artificial agent can autonomously acquire this intuition through interaction with the environment. We created a synthetic block stacking environment with physics simulation in which the agent can learn a policy end-to-end through trial and error. Thereby, we bypass to explicitly model physical knowledge within the policy. We are specifically interested in tasks that require the agent to reach a given goal state that may be different for every new trial. To this end, we propose a deep reinforcement learning framework that learns policies which are parametrized by a goal. We validated the model on a toy example navigating in a grid world with different target positions and in a block stacking task with different target structures of the final tower. In contrast to prior work, our policies show better generalization across different goals.

Includes members:
  Member "physical problem solving joint planning with symbolic geometric and dynamic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of knowledge representation in service robotics")
  Member "see the glass half full reasoning about liquid containers their volume and content":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust robotic pouring using audition and haptics")
  Member "to fall or not to fall a visual approach to physical stability prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "learning to poke by poking experiential learning of intuitive physics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "learning physical intuition of block towers by example":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "long term image boundary prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "making sense of sensory input")
  Member "newtonian image understanding unfolding the dynamics of objects in static images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "learning visual predictive models of physics for playing billiards":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action for better prediction")

Question: Please tell me which class the source paper 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)