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: "probabilistic frame induction"
  Abstract: In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, participants as latent topics and learns those frame and event transitions that best explain the text. The number of frames is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produced state-of-the-art results while substantially reducing engineering effort.

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 "open domain event extraction using neural latent variable models"):
  with members:
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint extraction of events and entities within a document context"
    - "joint learning templates and slots for event schema induction"
    - "using sentence level lstm language models for script inference"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "automatic event salience identification"):
  with members:
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "swat a system for detecting salient wikipedia entities in texts"
    - "implicit argument prediction with event knowledge"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "using sentence level lstm language models for script inference"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

Connected through hyperedge HE-4 (induced by source paper "graph based decoding for event sequencing and coreference resolution"):
  with members:
    - "solving hard coreference problems"
    - "end to end neural coreference resolution"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"
    - "using sentence level lstm language models for script inference"

Connected through hyperedge HE-5 (induced by source paper "learning scripts as hidden markov models"):
  with members:
    - "the bayesian structural em algorithm"

Connected through hyperedge HE-6 (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"
    - "learning frames from text with an unsupervised latent variable model"
    - "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-7 (induced by source paper "event representations with tensor based compositions"):
  with members:
    - "zero shot transfer learning for event extraction"
    - "distributed representations of sentences and documents"
    - "a study of entanglement in a categorical framework of natural language"
    - "evaluating neural word representations in tensor based compositional settings"
    - "learning semantic script knowledge with event embeddings"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-8 (induced by source paper "event schema induction using tensor factorization with back off"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "event representations with tensor based compositions"
    - "relation schema induction using tensor factorization with side information"
    - "neural semantic role labeling with dependency path embeddings"

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)