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: "neuralreg an end to end approach to referring expression generation"
  Abstract: Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines. Data and models are publicly available.

Connected through hyperedge HE-1 (induced by source paper "a formal analysis of multimodal referring strategies under common ground"):
  with members:
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "generating justifications for norm related agent decisions"
    - "multimodal attention branch network for perspective free sentence generation"
    - "multimodal continuation style architectures for human robot interaction"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "multimodal grounding for language processing"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "the something something video database for learning and evaluating visual common sense"

Connected through hyperedge HE-2 (induced by source paper "step by step separating planning from realization in neural data to text generation"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "learning neural templates for text generation"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "findings of the e2e nlg challenge"
    - "data to text generation with content selection and planning"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-3 (induced by source paper "be concise and precise synthesizing open domain entity descriptions from facts"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "generating fine grained open vocabulary entity type descriptions"
    - "get to the point summarization with pointer generator networks"
    - "a neural knowledge language model"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "video captioning with multi faceted attention"
    - "generating text from structured data with application to the biography domain"

Connected through hyperedge HE-4 (induced by source paper "neural data to text generation a comparison between pipeline and end to end architectures"):
  with members:
    - "designing a symbolic intermediate representation for neural surface realization"
    - "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"
    - "deep graph convolutional encoders for structured data to text generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "the e2e dataset new challenges for end to end generation"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"

Connected through hyperedge HE-5 (induced by source paper "improving quality and efficiency in plan based neural data to text generation"):
  with members:
    - "revisiting adversarial autoencoder for unsupervised word translation with cycle consistency and improved training"
    - "step by step separating planning from realization in neural data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "individual and domain adaptation in sentence planning for dialogue"

Connected through hyperedge HE-6 (induced by source paper "referring expression generation using entity profiles"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "neural text generation from structured data with application to the biography domain"
    - "a neural attention model for abstractive sentence summarization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "bengal an automatic benchmark generator for entity recognition and linking"):
  with members:
    - "probabilistic bag of hyperlinks model for entity linking"
    - "a survey of paraphrasing and textual entailment methods"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)