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: "generalization in generation a closer look at exposure bias"
  Abstract: Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time. We separate the contributions of the model and the learning framework to clarify the debate on consequences and review proposed counter-measures. In this light, we argue that generalization is the underlying property to address and propose unconditional generation as its fundamental benchmark. Finally, we combine latent variable modeling with a recent formulation of exploration in reinforcement learning to obtain a rigorous handling of true and generated contexts. Results on language modeling and variational sentence auto-encoding confirm the model's generalization capability.

Connected through hyperedge HE-1 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "the curious case of neural text degeneration"
    - "bertscore evaluating text generation with bert"
    - "on the use of bert for neural machine translation"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "moverscore text generation evaluating with contextualized embeddings and earth mover distance"

Connected through hyperedge HE-2 (induced by source paper "teacher student training for robust tacotron based tts"):
  with members:
    - "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"
    - "fastspeech fast robust and controllable text to speech"
    - "a new gan based end to end tts training algorithm"
    - "robust and fine grained prosody control of end to end speech synthesis"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "online and linear time attention by enforcing monotonic alignments"
    - "wavenet a generative model for raw audio"

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.LG(Machine Learning)