human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a good sample is hard to find noise injection sampling and self training for neural language generation models"):
  Abstract: Deep neural networks (DNN) are quickly becoming the de facto standard modeling method for many natural language generation (NLG) tasks. In order for such models to truly be useful, they must be capable of correctly generating utterances for novel meaning representations (MRs) at test time. In practice, even sophisticated DNNs with various forms of semantic control frequently fail to generate utterances faithful to the input MR. In this paper, we propose an architecture agnostic self-training method to sample novel MR/text utterance pairs to augment the original training data. Remarkably, after training on the augmented data, even simple encoder-decoder models with greedy decoding are capable of generating semantically correct utterances that are as good as state-of-the-art outputs in both automatic and human evaluations of quality.

Includes members:
  Member "evaluating the state of the art of end to end natural language generation the e2e nlg challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metaphoric paraphrase generation")
  Member "pragmatically informative text generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication based evaluation for natural language generation")
  Member "multi domain neural network language generation for spoken dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "sequence to sequence generation for spoken dialogue via deep syntax trees and strings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic natural language generation using dependency information")
  Member "noisy parallel approximate decoding for conditional recurrent language model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non autoregressive neural dialogue generation")
  Member "what to talk about and how selective generation using lstms with coarse to fine alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational template machine for data to text generation")
  Member "building end to end dialogue systems using generative hierarchical neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention over parameters for dialogue systems")
  Member "learning in the rational speech acts model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial language games for advanced natural language intelligence")

Question: Please tell me which class the source paper 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)