human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning dynamic knowledge graphs to generalize on text based games"):
  Abstract: Playing text-based games requires skill in processing natural language and in planning. Although a key goal for agents solving this task is to generalize across multiple games, most previous work has either focused on solving a single game or has tackled generalization with rule-based heuristics. In this work, we investigate how structured information in the form of a knowledge graph (KG) can facilitate effective planning and generalization. We introduce a novel transformer-based sequence-to-sequence model that constructs a "belief" KG from raw text observations of the environment, dynamically updating this belief graph at every game step as it receives new observations. To train this model to build useful graph representations, we introduce and analyze a set of graph-related pre-training tasks. We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games. Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6%.

Includes members:
  Member "graph constrained reinforcement learning for natural language action spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bringing stories alive generating interactive fiction worlds")
  Member "does order matter an empirical study on generating multiple keyphrases as a sequence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building dynamic knowledge graphs from text based games")
  Member "nail a general interactive fiction agent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph constrained reinforcement learning for natural language action spaces")
  Member "algorithmic improvements for deep reinforcement learning applied to interactive fiction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ecological semantics programming environments for situated language understanding")
  Member "interactive fiction games a colossal adventure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph constrained reinforcement learning for natural language action spaces")
  Member "using local knowledge graph construction to scale seq2seq models to multi document inputs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic knowledge graph construction for zero shot commonsense question answering")
  Member "on the variance of the adaptive learning rate and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning to speak and act in a fantasy text adventure game":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents")

Question: Please tell me which class the source paper 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)