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: "what can you do with a rock affordance extraction via word embeddings"
  Abstract: Autonomous agents must often detect affordances: the set of behaviors enabled by a situation. Affordance detection is particularly helpful in domains with large action spaces, allowing the agent to prune its search space by avoiding futile behaviors. This paper presents a method for affordance extraction via word embeddings trained on a Wikipedia corpus. The resulting word vectors are treated as a common knowledge database which can be queried using linear algebra. We apply this method to a reinforcement learning agent in a text-only environment and show that affordance-based action selection improves performance most of the time. Our method increases the computational complexity of each learning step but significantly reduces the total number of steps needed. In addition, the agent's action selections begin to resemble those a human would choose.

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 "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-5 (induced by source paper "towards finding longer proofs"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "meta learning of sequential strategies"
    - "grunge a grand unified atp challenge"
    - "analysing mathematical reasoning abilities of neural models"
    - "graph representations for higher order logic and theorem proving"
    - "learning to speak and act in a fantasy text adventure game"
    - "hammering mizar by learning clause guidance"

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 "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"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "divide and conquer reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "comprehensible context driven text game playing"):
  with members:
    - "language expansion in text based games"
    - "exploration by random network distillation"
    - "textworld a learning environment for text based games"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "convolutional sequence to sequence learning"
    - "text based adventures of the golovin ai agent"
    - "deep reinforcement learning for dialogue generation"
    - "prioritized experience replay"

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.AI(Artificial Intelligence)