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 "span model for open information extraction on accurate corpus"):
  Abstract: Open information extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE6) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of previous adopted sequence labeling formulization for n-ary Open IE. Our newly introduced model achieves new state-of-the-art performance on both benchmark evaluation datasets.

Includes members:
  Member "global greedy dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "dependency or span end to end uniform semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantics aware bert for language understanding")
  Member "head driven phrase structure grammar parsing on penn treebank":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parsing as pretraining")
  Member "graphene a context preserving open information extraction system":
    also connected through other hyperedges:
      (none)
  Member "a full end to end semantic role labeler syntax agnostic over syntax aware":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capturing argument interaction in semantic role labeling with capsule networks")
  Member "a span selection model for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "span based joint entity and relation extraction with transformer pre training")
  Member "jointly predicting predicates and arguments in neural semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spanbert improving pre training by representing and predicting spans")
  Member "neural open information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hybrid neural tagging model for open relation extraction")

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)