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 by the book an interactive game approach for action graph extraction from text"
  Abstract: Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from material science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A learning agent completes the game by executing the procedure correctly in a text-based simulated lab environment. The framework can complement existing approaches and enables richer forms of learning compared to static texts. We discuss potential limitations and advantages of the approach, and release a prototype proof-of-concept, hoping to encourage research in this direction.

Connected through hyperedge HE-1 (induced by source paper "ecological semantics programming environments for situated language understanding"):
  with members:
    - "bringing stories alive generating interactive fiction worlds"
    - "environmental drivers of systematicity and generalization in a situated agent"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "extending machine language models toward human level language understanding"
    - "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"
    - "human few shot learning of compositional instructions"
    - "do neural language representations learn physical commonsense"
    - "a survey of reinforcement learning informed by natural language"

Connected through hyperedge HE-2 (induced by source paper "annotating and extracting synthesis process of all solid state batteries from scientific literature"):
  with members:
    - "the materials science procedural text corpus annotating materials synthesis procedures with shallow semantic structures"
    - "scibert a pretrained language model for scientific text"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automatically extracting action graphs from materials science synthesis procedures"

Connected through hyperedge HE-3 (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"
    - "towards solving text based games by producing adaptive action spaces"
    - "playing text adventure games with graph based deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "the materials science procedural text corpus annotating materials synthesis procedures with shallow semantic structures"):
  with members:
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "automatically extracting action graphs from materials science synthesis procedures"
    - "extracting biomolecular interactions using semantic parsing of biomedical 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.LG(Machine Learning)