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: "bootstrapping lexical choice via multiple sequence alignment"
  Abstract: An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically, labor-intensive knowledge-based methods are used to construct the dictionary. We instead propose to acquire it automatically via a novel multiple-pass algorithm employing multiple-sequence alignment, a technique commonly used in bioinformatics. Crucially, our method leverages latent information contained in multi-parallel corpora -- datasets that supply several verbalizations of the corresponding semantics rather than just one. #R##N#We used our techniques to generate natural language versions of computer-generated mathematical proofs, with good results on both a per-component and overall-output basis. For example, in evaluations involving a dozen human judges, our system produced output whose readability and faithfulness to the semantic input rivaled that of a traditional generation system.

Connected through hyperedge HE-1 (induced by source paper "curate and generate a corpus and method for joint control of semantics and style in neural nlg"):
  with members:
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "opennmt neural machine translation toolkit"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

Connected through hyperedge HE-2 (induced by source paper "multi reference training with pseudo references for neural translation and text generation"):
  with members:
    - "classify or select neural architectures for extractive document summarization"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a neural attention model for abstractive sentence summarization"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "sequence to sequence video to text"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-3 (induced by source paper "a survey of paraphrasing and textual entailment methods"):
  with members:
    - "text relatedness based on a word thesaurus"
    - "from frequency to meaning vector space models of semantics"
    - "sentence compression as tree transduction"
    - "acquiring lexical paraphrases from a single corpus"
    - "learning to paraphrase an unsupervised approach using multiple sequence alignment"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

Connected through hyperedge HE-4 (induced by source paper "individual and domain adaptation in sentence planning for dialogue"):
  with members:
    - "learning content selection rules for generating object descriptions in dialogue"
    - "a flexible pragmatics driven language generator for animated agents"
    - "acquiring correct knowledge for natural language generation"
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "cue phrase classification using machine learning"

Connected through hyperedge HE-5 (induced by source paper "learning to paraphrase an unsupervised approach using multiple sequence alignment"):
  with members:
    - "finding consensus in speech recognition word error minimization and other applications of confusion networks"

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)