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: "towards solving text based games by producing adaptive action spaces"
  Abstract: To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success. Recent attempts at solving text-based games with deep reinforcement learning have focused on the latter, i.e., learning to act optimally when valid actions are known in advance. In this work, we propose to tackle the first task and train a model that generates the set of all valid commands for a given context. We try three generative models on a dataset generated with Textworld. The best model can generate valid commands which were unseen at training and achieve high $F_1$ score on the test set.

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"
    - "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-2 (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"
    - "counting to explore and generalize in text based games"
    - "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-3 (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"
    - "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 "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"
    - "counting to explore and generalize in text based games"
    - "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-5 (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"
    - "transfer in deep reinforcement learning using knowledge graphs"
    - "textworld a learning environment for text based games"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "playing by the book an interactive game approach for action graph extraction from text"

Connected through hyperedge HE-6 (induced by source paper "transfer in deep reinforcement learning using knowledge graphs"):
  with members:
    - "playing text adventure games with graph based deep reinforcement learning"
    - "progressive neural networks"
    - "what can you do with a rock affordance extraction via word embeddings"
    - "attend adapt and transfer attentive deep architecture for adaptive transfer from multiple sources in the same domain"
    - "deep reinforcement learning with a natural language action space"
    - "actor mimic deep multitask and transfer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "playing text adventure games with graph based deep reinforcement learning"):
  with members:
    - "textworld a learning environment for text based games"
    - "the natural language decathlon multitask learning as question answering"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "what can you do with a rock affordance extraction via word embeddings"
    - "deep reinforcement learning with a natural language action space"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)