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: "transfer in deep reinforcement learning using knowledge graphs"
  Abstract: Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy transfer. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.

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"
    - "a survey of reinforcement learning informed by natural language"
    - "exploration by random network distillation"
    - "using reinforcement learning to learn how to play text based games"
    - "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"
    - "using reinforcement learning to learn how to play text based games"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-3 (induced by source paper "bringing stories alive generating interactive fiction worlds"):
  with members:
    - "graph constrained reinforcement learning for natural language action spaces"
    - "generating interactive worlds with text"
    - "nail a general interactive fiction agent"
    - "interactive fiction games a colossal adventure"
    - "ctrl a conditional transformer language model for controllable generation"
    - "toward automated quest generation in text adventure games"
    - "albert a lite bert for self supervised learning of language representations"
    - "interactive language learning by question answering"

Connected through hyperedge HE-4 (induced by source paper "how to avoid being eaten by a grue exploration strategies for text adventure agents"):
  with members:
    - "graph constrained reinforcement learning for natural language action spaces"
    - "exploration based language learning for text based games"
    - "go explore a new approach for hard exploration problems"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "interactive fiction games a colossal adventure"
    - "action assembly sparse imitation learning for text based games with combinatorial action spaces"
    - "comprehensible context driven text game playing"
    - "textworld a learning environment for text based games"

Connected through hyperedge HE-5 (induced by source paper "interactive language learning by question answering"):
  with members:
    - "nail a general interactive fiction agent"
    - "learn how to cook a new recipe in a new house using map familiarization curriculum learning and common sense to learn families of text based adventure games"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "the web as a knowledge base for answering complex questions"
    - "think you have solved question answering try arc the ai2 reasoning challenge"

Connected through hyperedge HE-6 (induced by source paper "interactive fiction games a colossal adventure"):
  with members:
    - "nail a general interactive fiction agent"
    - "go explore a new approach for hard exploration problems"
    - "interactive language learning by question answering"
    - "learning to speak and act in a fantasy text adventure game"
    - "comprehensible context driven text game playing"
    - "backplay man muss immer umkehren"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-7 (induced by source paper "toward automated quest generation in text adventure games"):
  with members:
    - "nail a general interactive fiction agent"
    - "the curious case of neural text degeneration"
    - "interactive fiction games a colossal adventure"
    - "learning to speak and act in a fantasy text adventure game"
    - "textworld a learning environment for text based games"
    - "towards solving text based games by producing adaptive action spaces"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "playing by the book an interactive game approach for action graph extraction from text"

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)