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: "order planning neural text generation from structured data"
  Abstract: Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches do not model the order of contents during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In a biography, for example, the nationality of a person is typically mentioned before occupation in a biography. In this paper, we propose an order-planning text generation model to capture the relationship between different fields and use such relationship to make the generated text more fluent and smooth. We conducted experiments on the WikiBio dataset and achieve significantly higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores.

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"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"

Connected through hyperedge HE-2 (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"
    - "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 "long and diverse text generation with planning based hierarchical variational model"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "step by step separating planning from realization in neural data to text generation"
    - "towards generating long and coherent text with multi level latent variable models"
    - "a hierarchical latent structure for variational conversation modeling"
    - "texygen a benchmarking platform for text generation models"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "generating high quality and informative conversation responses with sequence to sequence models"

Connected through hyperedge HE-4 (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-5 (induced by source paper "automated chess commentator powered by neural chess engine"):
  with members:
    - "press space to fire automatic video game tutorial generation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "generating levels that teach mechanics"
    - "challenges in data to document generation"
    - "story generation from sequence of independent short descriptions"
    - "deepchess end to end deep neural network for automatic learning in chess"
    - "effective approaches to attention based neural machine translation"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-6 (induced by source paper "bootstrapping generators from noisy data"):
  with members:
    - "handling divergent reference texts when evaluating table to text generation"
    - "sentence simplification with deep reinforcement learning"
    - "what does attention in neural machine translation pay attention to"
    - "learning to generate one sentence biographies from wikidata"
    - "challenges in data to document generation"
    - "table to text generation by structure aware seq2seq learning"
    - "semantic tagging with deep residual networks"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-7 (induced by source paper "data to text generation with content selection and planning"):
  with members:
    - "bootstrapping generators from noisy data"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "learning to generate one sentence biographies from wikidata"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "table to text generation by structure aware seq2seq learning"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "text assisted insight ranking using context aware memory network"):
  with members:
    - "foresight rapid data exploration through guideposts"
    - "table to text generation by structure aware seq2seq learning"
    - "neural text generation from structured data with application to the biography domain"
    - "key value memory networks for directly reading documents"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "adam a method for stochastic optimization"

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)