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: "language model pre training for hierarchical document representations"
  Abstract: Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentation, and sentiment analysis. However, effective usage of such a large context can be difficult to learn, especially in the case where there is limited labeled data available. Building on the recent success of language model pretraining methods for learning flat representations of text, we propose algorithms for pre-training hierarchical document representations from unlabeled data. Unlike prior work, which has focused on pre-training contextual token representations or context-independent {sentence/paragraph} representations, our hierarchical document representations include fixed-length sentence/paragraph representations which integrate contextual information from the entire documents. Experiments on document segmentation, document-level question answering, and extractive document summarization demonstrate the effectiveness of the proposed pre-training algorithms.

Connected through hyperedge HE-1 (induced by source paper "hierarchical models vs transfer learning for document level sentiment classification"):
  with members:
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "regularizing and optimizing lstm language models"
    - "norec the norwegian review corpus"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "masking orchestration multi task pretraining for multi role dialogue representation learning"):
  with members:
    - "pretraining methods for dialog context representation learning"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "dialogue act recognition via crf attentive structured network"
    - "attention is all you need"
    - "dialogue act sequence labeling using hierarchical encoder with crf"

Connected through hyperedge HE-3 (induced by source paper "pretraining based natural language generation for text summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "deepchannel salience estimation by contrastive learning for extractive document summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "pretrained language models for sequential sentence classification"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "roberta a robustly optimized bert pretraining approach"
    - "unified language model pre training for natural language understanding and generation"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)