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 "dual co matching network for multi choice reading comprehension"):
  Abstract: Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question. Previous approaches usually only calculate question-aware passage representation and ignore passage-aware question representation when modeling the relationship between passage and question, which obviously cannot take the best of information between passage and question. In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. Besides, inspired by how human solve multi-choice questions, we integrate two reading strategies into our model: (i) passage sentence selection that finds the most salient supporting sentences to answer the question, (ii) answer option interaction that encodes the comparison information between answer options. DCMN integrated with the two strategies (DCMN+) obtains state-of-the-art results on five multi-choice reading comprehension datasets which are from different domains: RACE, SemEval-2018 Task 11, ROCStories, COIN, MCTest.

Includes members:
  Member "semantics aware bert for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "hierarchical contextualized representation for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep learning for named entity recognition")
  Member "convolutional spatial attention model for reading comprehension with multiple choice questions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gennet reading comprehension with multiple choice questions using generation and selection model")
  Member "option comparison network for multiple choice reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neuro symbolic architectures for context understanding")
  Member "sg net syntax guided machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do multi hop question answering systems know how to answer the single hop sub questions")
  Member "evidence sentence extraction for machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "lattice based transformer encoder for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical contextualized representation for named entity recognition")
  Member "head driven phrase structure grammar parsing on penn treebank":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parsing as pretraining")

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)