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: "data to text generation with content selection and planning"
  Abstract: Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model outperforms strong baselines improving the state-of-the-art on the recently released RotoWire dataset.

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 "sticking to the facts confident decoding for faithful data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "data to text generation with entity modeling"
    - "text generation with exemplar based adaptive decoding"
    - "handling divergent reference texts when evaluating table to text generation"
    - "calibration entropy rates and memory in language models"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "bottom up abstractive summarization"

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"
    - "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"
    - "order planning neural text generation from structured data"

Connected through hyperedge HE-4 (induced by source paper "encode tag realize high precision text editing"):
  with members:
    - "fine tune bert for extractive summarization"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "step by step separating planning from realization in neural data to text generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "learning neural templates for text generation"

Connected through hyperedge HE-5 (induced by source paper "text generation from knowledge graphs with graph transformers"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "graph to sequence learning using gated graph neural networks"
    - "paper abstract writing through editing mechanism"
    - "a graph to sequence model for amr to text generation"
    - "construction of the literature graph in semantic scholar"
    - "deep graph convolutional encoders for structured data to text generation"
    - "attention is all you need"
    - "neural amr sequence to sequence models for parsing and generation"

Connected through hyperedge HE-6 (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"
    - "attention is all you need"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "a hierarchical model for data to text generation"):
  with members:
    - "data to text generation with entity modeling"
    - "copy mechanism and tailored training for character based data to text generation"
    - "table2vec neural word and entity embeddings for table population and retrieval"
    - "tabvec table vectors for classification of web tables"
    - "learning neural templates for text generation"
    - "neural arithmetic logic units"
    - "a call for clarity in reporting bleu scores"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-8 (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"
    - "get to the point summarization with pointer generator 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)