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: "playing text adventure games with graph based deep reinforcement learning"
  Abstract: Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. This graph is used to prune the action space, enabling more efficient exploration. The question of which action to take can be reduced to a question-answering task, a form of transfer learning that pre-trains certain parts of our architecture. In experiments using the TextWorld framework, we show that our proposed technique can learn a control policy faster than baseline alternatives. We have also open-sourced our code at https://github.com/rajammanabrolu/KG-DQN.

Connected through hyperedge HE-1 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games"):
  with members:
    - "graph constrained reinforcement learning for natural language action spaces"
    - "does order matter an empirical study on generating multiple keyphrases as a sequence"
    - "nail a general interactive fiction agent"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "interactive fiction games a colossal adventure"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "on the variance of the adaptive learning rate and beyond"
    - "learning to speak and act in a fantasy text adventure game"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "using reinforcement learning to learn how to play text based games"

Connected through hyperedge HE-4 (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-5 (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"
    - "transfer in deep reinforcement learning using knowledge graphs"

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"
    - "transfer in deep reinforcement learning using knowledge graphs"
    - "backplay man muss immer umkehren"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-7 (induced by source paper "generating interactive worlds with text"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "learning to speak and act in a fantasy text adventure game"
    - "gender bias in contextualized word embeddings"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "understanding the origins of bias in word embeddings"
    - "design design inspiration from generative networks"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "algorithmic improvements for deep reinforcement learning applied to interactive fiction"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "textworld a learning environment for text based games"
    - "the text based adventure ai competition"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "towards solving text based games by producing adaptive action spaces"
    - "counting to explore and generalize in text based games"
    - "divide and conquer reinforcement learning"
    - "what can you do with a rock affordance extraction via word embeddings"

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)