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: "docbert bert for document classification"
  Abstract: We present, to our knowledge, the first application of BERT to document classification. A few characteristics of the task might lead one to think that BERT is not the most appropriate model: syntactic structures matter less for content categories, documents can often be longer than typical BERT input, and documents often have multiple labels. Nevertheless, we show that a straightforward classification model using BERT is able to achieve the state of the art across four popular datasets. To address the computational expense associated with BERT inference, we distill knowledge from BERT-large to small bidirectional LSTMs, reaching BERT-base parity on multiple datasets using 30x fewer parameters. The primary contribution of our paper is improved baselines that can provide the foundation for future work.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-2 (induced by source paper "pel bert a joint model for protocol entity linking"):
  with members:
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "bridging the gap between training and inference for neural machine translation"
    - "how to fine tune bert for text classification"
    - "claim extraction in biomedical publications using deep discourse model and transfer learning"
    - "deep contextualized word representations"
    - "leveraging textual specifications for grammar based fuzzing of network protocols"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "personalized patent claim generation and measurement"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "select and attend towards controllable content selection in text generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "patentbert patent classification with fine tuning a pre trained bert model"
    - "scibert a pretrained language model for scientific text"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "defending against neural fake news"

Connected through hyperedge HE-4 (induced by source paper "phenotyping of clinical notes with improved document classification models using contextualized neural language models"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "transformer xl attentive language models beyond a fixed length context"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "publicly available clinical bert embeddings"
    - "clinical text classification with rule based features and knowledge guided convolutional neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "constructing artificial data for fine tuning for low resource biomedical text tagging with applications in pico annotation"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "structured multi label biomedical text tagging via attentive neural tree decoding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "a c lstm neural network for text classification"

Connected through hyperedge HE-6 (induced by source paper "hierarchical transformers for long document classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "scdv sparse composite document vectors using soft clustering over distributional representations"
    - "character level convolutional networks for text classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "long length legal document classification"):
  with members:
    - "mitigating uncertainty in document classification"
    - "sgm sequence generation model for multi label classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "enriching bert with knowledge graph embeddings for document classification"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "ernie enhanced language representation with informative entities"
    - "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)