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 "joint entity linking with deep reinforcement learning"):
  Abstract: Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise scores between all candidate entities and select the most relevant group of entities as the final result. In this process, the consistency among wrong entities as well as that among right ones are involved, which may introduce noise data and increase the model complexity. Second, the cues of previously disambiguated entities, which could contribute to the disambiguation of the subsequent mentions, are usually ignored by previous models. To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective. Our model makes full use of the previous referred entities and explores the long-term influence of current selection on subsequent decisions. We conduct experiments on different types of datasets, the results show that our model outperforms state-of-the-art systems and has better generalization performance.

Includes members:
  Member "neural collective entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences")
  Member "improving entity linking by modeling latent relations between mentions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "deep reinforcement learning for chinese zero pronoun resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating persona consistent dialogues by exploiting natural language inference")
  Member "pair linking for collective entity disambiguation two could be better than all":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "obj glove scene based contextual object embedding")
  Member "deeppath a reinforcement learning method for knowledge graph reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "towards end to end reinforcement learning of dialogue agents for information access":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "joint learning of the embedding of words and entities for named entity disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning cross context entity representations from text")

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)