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: "the thing that we tried didn t work very well deictic representation in reinforcement learning"
  Abstract: Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be a viable alternative: they promise generalization while allowing the use of existing reinforcement-learning methods. Yet, there are few experiments on learning with deictic representations reported in the literature. In this paper we explore the effectiveness of two forms of deictic representation and a na\"{i}ve propositional representation in a simple blocks-world domain. We find, empirically, that the deictic representations actually worsen learning performance. We conclude with a discussion of possible causes of these results and strategies for more effective learning in domains with objects.

Connected through hyperedge HE-1 (induced by source paper "optimization methods for interpretable differentiable decision trees in reinforcement learning"):
  with members:
    - "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation"
    - "prolonets neural encoding human experts domain knowledge to warm start reinforcement learning"
    - "is attention interpretable"
    - "learning dexterous in hand manipulation"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "an optimization approach to learning falling rule lists"
    - "deep reinforcement learning a brief survey"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"

Connected through hyperedge HE-2 (induced by source paper "learning portable representations for high level planning"):
  with members:
    - "composable planning with attributes"
    - "active exploration for learning symbolic representations"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-3 (induced by source paper "option discovery in hierarchical reinforcement learning using spatio temporal clustering"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "prioritized experience replay"
    - "action conditional video prediction using deep networks in atari games"
    - "the arcade learning environment an evaluation platform for general agents"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-4 (induced by source paper "hierarchical reinforcement learning using spatio temporal abstractions and deep neural networks"):
  with members:
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "prioritized experience replay"
    - "action conditional video prediction using deep networks in atari games"
    - "the arcade learning environment an evaluation platform for general agents"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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.LG(Machine Learning)