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 "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models"):
  Abstract: In Natural Language Generation (NLG), End-to-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of carefully annotated data to reach satisfactory performance. However, acquiring such datasets for every new NLG application is a tedious and time-consuming task. In this paper, we propose a semi-supervised deep learning scheme that can learn from non-annotated data and annotated data when available. It uses an NLG and a Natural Language Understanding (NLU) sequence-to-sequence models which are learned jointly to compensate for the lack of annotation. Experiments on two benchmark datasets show that, with limited amount of annotated data, the method can achieve very competitive results while not using any pre-processing or re-scoring tricks. These findings open the way to the exploitation of non-annotated datasets which is the current bottleneck for the E2E NLG system development to new applications.

Includes members:
  Member "understanding straight through estimator in training activation quantized neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "char2char generation with reranking for the e2e nlg challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "end to end feedback loss in speech chain framework via straight through estimator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "end to end content and plan selection for data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "paraphrase generation with latent bag of words")
  Member "a deep ensemble model with slot alignment for sequence to sequence natural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic natural language generation using dependency information")
  Member "survey of the state of the art in natural language generation core tasks applications and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "neural machine translation with gumbel greedy decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")
  Member "listening while speaking speech chain by deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self supervised speech recognition via local prior matching")

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)