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 "cooperative reasoning on knowledge graph and corpus a multi agentreinforcement learning approach"):
  Abstract: Knowledge-graph-based reasoning has drawn a lot of attention due to its interpretability. However, previous methods suffer from the incompleteness of the knowledge graph, namely the interested link or entity that can be missing in the knowledge graph(explicit missing). Also, most previous models assume the distance between the target and source entity is short, which is not true on a real-world KG like Freebase(implicit missing). The sensitivity to the incompleteness of KG and the incapability to capture the long-distance link between entities have limited the performance of these models on large KG. In this paper, we propose a model that leverages the text corpus to cure such limitations, either the explicit or implicit missing links. We model the question answering on KG as a cooperative task between two agents, a knowledge graph reasoning agent and an information extraction agent. Each agent learns its skill to complete its own task, hopping on KG or select knowledge from the corpus, via maximizing the reward for correctly answering the question. The reasoning agent decides how to find an equivalent path for the given entity and relation. The extraction agent provide shortcut for long-distance target entity or provide missing relations for explicit missing links with messages from the reasoning agent. Through such cooperative reward design, our model can augment the incomplete KG strategically while not introduce much unnecessary noise that could enlarge the search space and lower the performance.

Includes members:
  Member "two body problem collaborative visual task completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "audio visual embodied navigation")
  Member "learning when not to answer a ternary reward structure for reinforcement learning based question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text as environment a deep reinforcement learning text readability assessment model")
  Member "deep communicating agents for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "learning beyond datasets knowledge graph augmented neural networks for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmenting neural machine translation with knowledge graphs")
  Member "multi hop knowledge graph reasoning with reward shaping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "open domain question answering using early fusion of knowledge bases and text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "m walk learning to walk over graphs using monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning")
  Member "collaborative deep reinforcement learning for joint object search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation")

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)