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: "counting to explore and generalize in text based games"
  Abstract: We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments. We show promising results on a set of generated text-based games of varying difficulty where the goal is to collect a coin located at the end of a chain of rooms. In contrast to previous text-based RL approaches, we observe that our agent learns policies that generalize to unseen games of greater difficulty.

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 "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-3 (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-4 (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"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "divide and conquer reinforcement learning"
    - "what can you do with a rock affordance extraction via word embeddings"

Connected through hyperedge HE-5 (induced by source paper "action assembly sparse imitation learning for text based games with combinatorial action spaces"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "towards solving text based games by producing adaptive action spaces"
    - "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-6 (induced by source paper "ledeepchef deep reinforcement learning agent for families of text based games"):
  with members:
    - "comprehensible context driven text game playing"
    - "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"
    - "textworld a learning environment for text based games"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "towards solving text based games by producing adaptive action spaces"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "generating diverse numbers of diverse keyphrases"
    - "text based adventures of the golovin ai agent"

Connected through hyperedge HE-7 (induced by source paper "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"):
  with members:
    - "textworld a learning environment for text based games"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "text based adventures of the golovin ai agent"
    - "what can you do with a rock affordance extraction via word embeddings"
    - "deep reinforcement learning for dialogue generation"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-8 (induced by source paper "nail a general interactive fiction agent"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "the text based adventure ai competition"
    - "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"

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)