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 "what do entity centric models learn insights from entity linking in multi party dialogue"):
  Abstract: Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations have been proposed. Such entity-centric models have shown empirical success, but we still know little about why. In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4). We show that these models outperform the state of the art on this task, and that they do better on lower frequency entities than a counterpart model that is not entity-centric, with the same model size. We argue that making models entity-centric naturally fosters good architectural decisions. However, we also show that these models do not really build entity representations and that they make poor use of linguistic context. These negative results underscore the need for model analysis, to test whether the motivations for particular architectures are borne out in how models behave when deployed.

Includes members:
  Member "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "amore upf at semeval 2018 task 4 bilstm with entity library":
    also connected through other hyperedges:
      (none)
  Member "evaluating theory of mind in question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "what you can cram into a single vector probing sentence embeddings for linguistic properties":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "relnet end to end modeling of entities relations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "procedural reasoning networks for understanding multimodal procedures")
  Member "improving coreference resolution by learning entity level distributed representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi task learning based neural bridging reference resolution")
  Member "capturing semantic similarity for entity linking with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pel bert a joint model for protocol entity linking")
  Member "representation of linguistic form and function in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on architectures for including visual information in neural language models for image description")

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)