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 "revisiting joint modeling of cross document entity and event coreference resolution"):
  Abstract: Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to within-document entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al (2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.

Includes members:
  Member "hotpotqa a dataset for diverse explainable multi hop question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "higher order coreference resolution with coarse to fine inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiplex word embeddings for selectional preference acquisition")
  Member "end to end neural coreference resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain independent extraction of scientific concepts from research articles")
  Member "event coreference resolution by iteratively unfolding inter dependencies among events":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resolving event coreference with supervised representation learning and clustering oriented regularization")
  Member "constructing datasets for multi hop reading comprehension across documents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "improving coreference resolution by learning entity level distributed representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi task learning based neural bridging reference resolution")
  Member "a hierarchical distance dependent bayesian model for event coreference resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "natural language processing for information extraction")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)