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 "quantifying exposure bias for neural language generation"):
  Abstract: The exposure bias problem refers to the training-generation discrepancy, caused by teacher forcing, in maximum likelihood estimation (MLE) training for auto-regressive neural network language models (LM). It has been regarded as a central problem for neural language generation (NLG) model training. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore` to alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we first identify the self-recovery ability of MLE-trained LM, which casts doubt on the seriousness of exposure bias. We then propose sequence-level (EB-bleu) and word-level (EB-C) metrics to quantify the impact of exposure bias. We conduct experiments for the LSTM/transformer model, in both real and synthetic settings. In addition to the unconditional NLG task, we also include results for a seq2seq machine translation task. Surprisingly, all our measurements indicate that removing the training-generation discrepancy only brings very little performance gain. In our analysis, we hypothesise that although there exist a mismatch between the model distribution and the data distribution, the mismatch is still in the model's "comfortable zone", and is not big enough to induce significant performance loss.

Includes members:
  Member "bridging the gap between training and inference for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "transformer xl attentive language models beyond a fixed length context":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "on accurate evaluation of gans for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "towards diverse text generation with inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self adversarial learning with comparative discrimination for text generation")
  Member "texygen a benchmarking platform for text generation models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "evaluating text gans as language models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi object tracking via end to end tracklet searching and ranking")
  Member "language gans falling short":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "adaptive input representations for neural language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "time aware large kernel convolutions")

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.LG(Machine Learning)