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 "the non utility of structural features in bilstm based dependency parsers"):
  Abstract: Classical non-neural dependency parsers put considerable effort on the design of feature functions. Especially, they benefit from information coming from structural features, such as features drawn from neighboring tokens in the dependency tree. In contrast, their BiLSTM-based successors achieve state-of-the-art performance without explicit information about the structural context. In this paper we aim to answer the question: How much structural context are the BiLSTM representations able to capture implicitly? We show that features drawn from partial subtrees become redundant when the BiLSTMs are used. We provide a deep insight into information flow in transition- and graph-based neural architectures to demonstrate where the implicit information comes from when the parsers make their decisions. Finally, with model ablations we demonstrate that the structural context is not only present in the models, but it significantly influences their performance.

Includes members:
  Member "recursive subtree composition in lstm based dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what should do can lstms learn when parsing auxiliary verb constructions")
  Member "what s going on in neural constituency parsers an analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "transition based parsing with lighter feed forward networks":
    also connected through other hyperedges:
      (none)
  Member "why comparing single performance scores does not allow to draw conclusions about machine learning approaches":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "global transition based non projective dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic expressive capacity with bounded memory")
  Member "deep rnns encode soft hierarchical syntax":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "what you can cram into a single vector probing sentence embeddings for linguistic properties":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "an investigation of the interactions between pre trained word embeddings character models and pos tags in dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing")

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)