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: "learning frames from text with an unsupervised latent variable model"
  Abstract: We develop a probabilistic latent-variable model to discover semantic frames---types of events and their participants---from corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the model learns, and compare it to FrameNet, noting it learns some novel and interesting frames. This document also contains a discussion of inference issues, including concentration parameter learning; and a small-scale error analysis of syntactic parsing accuracy.

Connected through hyperedge HE-1 (induced by source paper "watset local global graph clustering with applications in sense and frame induction"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "unsupervised semantic frame induction using triclustering"
    - "a framework for enriching lexical semantic resources with distributional semantics"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "improving hypernymy extraction with distributional semantic classes"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "human and machine judgements for russian semantic relatedness"

Connected through hyperedge HE-2 (induced by source paper "unsupervised semantic frame induction using triclustering"):
  with members:
    - "watset automatic induction of synsets from a graph of synonyms"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "towards a science of human stories using sentiment analysis and emotional arcs to understand the building blocks of complex social systems"):
  with members:
    - "ultradense word embeddings by orthogonal transformation"
    - "boundary based mwe segmentation with text partitioning"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"
    - "probabilistic frame induction"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "unsupervised induction of semantic roles within a reconstruction error minimization framework"):
  with members:
    - "conditional random field autoencoders for unsupervised structured prediction"
    - "efficient estimation of word representations in vector space"
    - "cross lingual annotation projection of semantic roles"
    - "unsupervised search based structured prediction"

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)