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: "trainable methods for surface natural language generation"
  Abstract: We present three systems for surface natural language generation that are trainable from annotated corpora. The first two systems, called NLG1 and NLG2, require a corpus marked only with domain-specific semantic attributes, while the last system, called NLG3, requires a corpus marked with both semantic attributes and syntactic dependency information. All systems attempt to produce a grammatical natural language phrase from a domain-specific semantic representation. NLG1 serves a baseline system and uses phrase frequencies to generate a whole phrase in one step, while NLG2 and NLG3 use maximum entropy probability models to individually generate each word in the phrase. The systems NLG2 and NLG3 learn to determine both the word choice and the word order of the phrase. We present experiments in which we generate phrases to describe flights in the air travel domain.

Connected through hyperedge HE-1 (induced by source paper "strategies for structuring story generation"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "generating wikipedia by summarizing long sequences"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "higher order coreference resolution with coarse to fine inference"
    - "building language models for text with named entities"

Connected through hyperedge HE-2 (induced by source paper "extracting linguistic resources from the web for concept to text generation"):
  with members:
    - "generating natural language descriptions from owl ontologies the natural owl system"
    - "a survey of paraphrasing and textual entailment methods"
    - "acquiring correct knowledge for natural language generation"

Connected through hyperedge HE-3 (induced by source paper "survey of the state of the art in natural language generation core tasks applications and evaluation"):
  with members:
    - "toward controlled generation of text"
    - "controlling linguistic style aspects in neural language generation"
    - "affect lm a neural language model for customizable affective text generation"
    - "affective neural response generation"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "a persona based neural conversation model"
    - "write a classifier predicting visual classifiers from unstructured text descriptions"
    - "query focused opinion summarization for user generated content"

Connected through hyperedge HE-4 (induced by source paper "neural based natural language generation in dialogue using rnn encoder decoder with semantic aggregation"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "a network based end to end trainable task oriented dialogue system"
    - "a context aware natural language generator for dialogue systems"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "semantic refinement gru based neural language generation for spoken dialogue systems"):
  with members:
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "a network based end to end trainable task oriented dialogue system"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "from captions to visual concepts and back"):
  with members:
    - "show attend and tell neural image caption generation with visual attention"
    - "phrase based image captioning"
    - "microsoft coco captions data collection and evaluation server"
    - "very deep convolutional networks for large scale image recognition"
    - "on learning to localize objects with minimal supervision"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "modelling visualising and summarising documents with a single convolutional neural network"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-7 (induced by source paper "generating natural language descriptions from owl ontologies the natural owl system"):
  with members:
    - "content modeling using latent permutations"
    - "catching the drift probabilistic content models with applications to generation and summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "a flexible shallow approach to text generation"

Connected through hyperedge HE-8 (induced by source paper "learning to order facts for discourse planning in natural language generation"):
  with members:
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "generating multilingual personalized descriptions of museum exhibits the m piro project"
    - "an application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email"

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)