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: "capturing reliable fine grained sentiment associations by crowdsourcing and best worst scaling"
  Abstract: Access to word-sentiment associations is useful for many applications, including sentiment analysis, stance detection, and linguistic analysis. However, manually assigning fine-grained sentiment association scores to words has many challenges with respect to keeping annotations consistent. We apply the annotation technique of Best-Worst Scaling to obtain real-valued sentiment association scores for words and phrases in three different domains: general English, English Twitter, and Arabic Twitter. We show that on all three domains the ranking of words by sentiment remains remarkably consistent even when the annotation process is repeated with a different set of annotators. We also, for the first time, determine the minimum difference in sentiment association that is perceptible to native speakers of a language.

Connected through hyperedge HE-1 (induced by source paper "text generation from knowledge graphs with graph transformers"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "graph to sequence learning using gated graph neural networks"
    - "paper abstract writing through editing mechanism"
    - "a graph to sequence model for amr to text generation"
    - "construction of the literature graph in semantic scholar"
    - "deep graph convolutional encoders for structured data to text generation"
    - "data to text generation with content selection and planning"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "sentiment composition of words with opposing polarities"):
  with members:
    - "the effect of negators modals and degree adverbs on sentiment composition"
    - "improved semantic representations from tree structured long short term memory networks"
    - "a statistical parsing framework for sentiment classification"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-3 (induced by source paper "the effect of negators modals and degree adverbs on sentiment composition"):
  with members:
    - "sentiment composition of words with opposing polarities"
    - "stance and sentiment in tweets"
    - "crowdsourcing a word emotion association lexicon"
    - "measuring praise and criticism inference of semantic orientation from association"

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)