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: "table to text generation by structure aware seq2seq learning"
  Abstract: Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on this https URL

Connected through hyperedge HE-1 (induced by source paper "stochastic natural language generation using dependency information"):
  with members:
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "findings of the e2e nlg challenge"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "order planning neural text generation from structured data"
    - "the e2e dataset new challenges for end to end generation"
    - "neural text generation from structured data with application to the biography domain"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"

Connected through hyperedge HE-2 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "tabnet attentive interpretable tabular learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "global explanations of neural networks mapping the landscape of predictions"
    - "deep neural decision trees"
    - "consistent individualized feature attribution for tree ensembles"
    - "compositional attention networks for machine reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive neural trees"
    - "xgboost scalable gpu accelerated learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "key fact as pivot a two stage model for low resource table to text generation"):
  with members:
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "universal neural machine translation for extremely low resource languages"
    - "sgm sequence generation model for multi label classification"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "phrase based neural unsupervised machine translation"
    - "data to text generation with content selection and planning"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "table to text natural language generation with unseen schemas"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "domain adaptive dialog generation via meta learning"
    - "domain adaptation of neural machine translation by lexicon induction"
    - "step by step separating planning from realization in neural data to text generation"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "natural language to structured query generation via meta learning"
    - "data to text generation with content selection and planning"

Connected through hyperedge HE-7 (induced by source paper "pomo generating entity specific post modifiers in context"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "challenges in data to document generation"

Connected through hyperedge HE-8 (induced by source paper "two birds one stone a simple unified model for text generation from structured and unstructured data"):
  with members:
    - "learning to generate questions by learningwhat not to generate"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "improving neural question generation using answer separation"
    - "sequential copying networks"
    - "learning to ask neural question generation for reading comprehension"
    - "on the state of the art of evaluation in neural language models"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "neural question generation from text a preliminary study"

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)