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: "end to end content and plan selection for data to text generation"
  Abstract: Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training. An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation.

Connected through hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "topic guided variational autoencoders for text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "learning neural templates for text generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "stochastic natural language generation using dependency information"):
  with members:
    - "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"
    - "table to text generation by structure aware seq2seq learning"
    - "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-3 (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"
    - "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"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural generation for czech data and baselines"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "rdf2pt generating brazilian portuguese texts from rdf data"
    - "are all languages equally hard to language model"
    - "predicting target language ccg supertags improves neural machine translation"
    - "modeling target side inflection in neural machine translation"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "topic aware neural response generation"

Connected through hyperedge HE-5 (induced by source paper "automatic quality estimation for natural language generation ranting jointly rating and ranking"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "reference less quality estimation of text simplification systems"
    - "a practical approach to dialogue response generation in closed domains"
    - "referenceless quality estimation for natural language generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"
    - "challenges in data to document generation"
    - "adversarial evaluation of dialogue models"

Connected through hyperedge HE-6 (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"
    - "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"
    - "learning to generate one sentence biographies from wikidata"
    - "challenges in data to document generation"

Connected through hyperedge HE-7 (induced by source paper "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "neural machine translation with gumbel greedy decoding"
    - "listening while speaking speech chain by deep learning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-8 (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"
    - "neuralreg an end to end approach to referring expression 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"

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)