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 to generate one sentence biographies from wikidata"
  Abstract: We investigate the generation of one-sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries. Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts. The model achieves a BLEU score of 41, improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline. Human preference evaluation suggests the model is nearly as good as the Wikipedia reference. Manual analysis explores content selection, suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information.

Connected through hyperedge HE-1 (induced by source paper "variational template machine for data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "using monolingual data in neural machine translation a systematic study"
    - "the curious case of neural text degeneration"
    - "table to text generation with effective hierarchical encoder on three dimensions row column and time"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"

Connected through hyperedge HE-2 (induced by source paper "paperrobot incremental draft generation of scientific ideas"):
  with members:
    - "sql to text generation with graph to sequence model"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "learning to generate wikipedia summaries for underserved languages from wikidata"
    - "describing a knowledge base"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "hierarchical neural story generation"
    - "entity aware image caption generation"
    - "neural text generation a practical guide"

Connected through hyperedge HE-3 (induced by source paper "sentence level content planning and style specification for neural text generation"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "argument generation with retrieval planning and realization"
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural argument generation augmented with externally retrieved evidence"
    - "hierarchical neural story generation"

Connected through hyperedge HE-4 (induced by source paper "revisiting challenges in data to text generation with fact grounding"):
  with members:
    - "learning to select track and generate for data to text"
    - "data to text generation with entity modeling"
    - "step by step separating planning from realization in neural data to text generation"
    - "end to end content and plan selection for data to text generation"
    - "describing a knowledge base"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "data to text generation with content selection and planning"
    - "challenges in data to document generation"

Connected through hyperedge HE-5 (induced by source paper "eventkg the hub of event knowledge on the web and biographical timeline generation"):
  with members:
    - "towards better understanding researcher strategies in cross lingual event analytics"
    - "eventkg tl creating cross lingual timelines from an event centric knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "multiwiki interlingual text passage alignment in wikipedia"
    - "ongoing events in wikipedia a cross lingual case study"
    - "neural text generation from structured data with application to the biography domain"
    - "history by diversity helping historians search news archives"
    - "timemachine timeline generation for knowledge base entities"

Connected through hyperedge HE-6 (induced by source paper "wikidata from a research perspective a systematic mapping study of wikidata"):
  with members:
    - "using word embeddings for visual data exploration with ontodia and wikidata"
    - "linking imagenet wordnet synsets with wikidata"
    - "guidelines for systematic mapping studies in security engineering"
    - "wikidata vandalism detection the loganberry vandalism detector at wsdm cup 2017"
    - "overview of the wikidata vandalism detection task at wsdm cup 2017"
    - "predicting completeness in knowledge bases"
    - "building automated vandalism detection tools for wikidata"
    - "wikidata through the eyes of dbpedia"

Connected through hyperedge HE-7 (induced by source paper "entity summarization state of the art and future challenges"):
  with members:
    - "eventkg the hub of event knowledge on the web and biographical timeline generation"
    - "neural wikipedian generating textual summaries from knowledge base triples"
    - "neural text generation from structured data with application to the biography domain"
    - "graph summarization methods and applications a survey"
    - "timemachine timeline generation for knowledge base entities"
    - "leveraging usage data for linked data movie entity summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-8 (induced by source paper "an edit centric approach for wikipedia article quality assessment"):
  with members:
    - "learning to represent edits"
    - "effective approaches to attention based neural machine translation"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural 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)