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 "content differences in syntactic and semantic representations"):
  Abstract: Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA's distinction between a Scene and a non-Scene; (2) UCCA's distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.

Includes members:
  Member "syntactic scaffolds for semantic structures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalizing natural language analysis through span relation representations")
  Member "universal dependency parsing with a general transition based dag parser":
    also connected through other hyperedges:
      (none)
  Member "multitask parsing across semantic representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "broad coverage semantic parsing as transduction")
  Member "simple and effective text simplification using semantic and neural methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "made for each other broad coverage semantic structures meet preposition supersenses")
  Member "linguistically informed self attention for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmenting visual question answering with semantic frame information in a multitask learning approach")
  Member "a transition based directed acyclic graph parser for ucca":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers")
  Member "hume human ucca based evaluation of machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automatically extracting challenge sets for non local phenomena neural machine translation")
  Member "an incremental parser for abstract meaning representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "amr similarity metrics from principles")

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)