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: "using reinforcement learning to learn how to play text based games"
  Abstract: The ability to learn optimal control policies in systems where action space is defined by sentences in natural language would allow many interesting real-world applications such as automatic optimisation of dialogue systems. Text-based games with multiple endings and rewards are a promising platform for this task, since their feedback allows us to employ reinforcement learning techniques to jointly learn text representations and control policies. We present a general text game playing agent, testing its generalisation and transfer learning performance and showing its ability to play multiple games at once. We also present pyfiction, an open-source library for universal access to different text games that could, together with our agent that implements its interface, serve as a baseline for future research.

Connected through hyperedge HE-1 (induced by source paper "exploration based language learning for text based games"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "action assembly sparse imitation learning for text based games with combinatorial action spaces"
    - "transfer in deep reinforcement learning using knowledge graphs"
    - "a survey of reinforcement learning informed by natural language"
    - "exploration by random network distillation"
    - "textworld a learning environment for text based games"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "learning montezuma s revenge from a single demonstration"

Connected through hyperedge HE-2 (induced by source paper "graph constrained reinforcement learning for natural language action spaces"):
  with members:
    - "nail a general interactive fiction agent"
    - "interactive fiction games a colossal adventure"
    - "toward automated quest generation in text adventure games"
    - "interactive language learning by question answering"
    - "learning to speak and act in a fantasy text adventure game"
    - "comprehensible context driven text game playing"
    - "transfer in deep reinforcement learning using knowledge graphs"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-3 (induced by source paper "action assembly sparse imitation learning for text based games with combinatorial action spaces"):
  with members:
    - "textworld a learning environment for text based games"
    - "towards solving text based games by producing adaptive action spaces"
    - "counting to explore and generalize in text based games"
    - "an actor critic algorithm for sequence prediction"
    - "deep reinforcement learning in large discrete action spaces"
    - "deep reinforcement learning with a natural language action space"
    - "language understanding for text based games using deep reinforcement learning"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "nail a general interactive fiction agent"):
  with members:
    - "the text based adventure ai competition"
    - "counting to explore and generalize in text based games"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "text based adventures of the golovin ai agent"
    - "what can you do with a rock affordance extraction via word embeddings"
    - "skip thought vectors"
    - "deep reinforcement learning with a natural language action space"
    - "language understanding for text based games using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-6 (induced by source paper "baselines for reinforcement learning in text games"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "prioritized experience replay"
    - "language understanding for text based games using deep reinforcement learning"
    - "rmsprop and equilibrated adaptive learning rates for non convex optimization"
    - "learning to win by reading manuals in a monte carlo framework"
    - "distributed representations of words and phrases and their compositionality"

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.CL(Computation and Language)