human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "getting more out of syntax with props"
  Abstract: Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic post-processing or to information loss. To directly address the needs of semantic applications, we present PropS -- an output representation designed to explicitly and uniformly express much of the proposition structure which is implied from syntax, and an associated tool for extracting it from dependency trees.

Connected through hyperedge HE-1 (induced by source paper "span model for open information extraction on accurate corpus"):
  with members:
    - "global greedy dependency parsing"
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "graphene a context preserving open information extraction system"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "a span selection model for semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "neural open information extraction"

Connected through hyperedge HE-2 (induced by source paper "content differences in syntactic and semantic representations"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "universal dependency parsing with a general transition based dag parser"
    - "multitask parsing across semantic representations"
    - "simple and effective text simplification using semantic and neural methods"
    - "linguistically informed self attention for semantic role labeling"
    - "a transition based directed acyclic graph parser for ucca"
    - "hume human ucca based evaluation of machine translation"
    - "an incremental parser for abstract meaning representation"

Connected through hyperedge HE-3 (induced by source paper "learning open information extraction of implicit relations from reading comprehension datasets"):
  with members:
    - "transforming question answering datasets into natural language inference datasets"
    - "a survey on open information extraction"
    - "neural open information extraction"
    - "opennmt open source toolkit for neural machine translation"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "improving open information extraction via iterative rank aware learning"):
  with members:
    - "supervised neural models revitalize the open relation extraction"
    - "logician a unified end to end neural approach for open domain information extraction"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "neural open information extraction"
    - "training very deep networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "dependency based hybrid trees for semantic parsing"):
  with members:
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "universal semantic parsing"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "learning structured natural language representations for semantic parsing"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "language to logical form with neural attention"
    - "data recombination for neural semantic parsing"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "a survey on open information extraction"):
  with members:
    - "graphene semantically linked propositions in open information extraction"
    - "analysing errors of open information extraction systems"

Connected through hyperedge HE-7 (induced by source paper "wire57 a fine grained benchmark for open information extraction"):
  with members:
    - "analysing errors of open information extraction systems"

Question: Please tell me which class the center node 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)