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: "pubmed 200k rct a dataset for sequential sentence classification in medical abstracts"
  Abstract: We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short-text classification (i.e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.

Connected through hyperedge HE-1 (induced by source paper "domain independent extraction of scientific concepts from research articles"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "investigating correlations of inter coder agreement and machine annotation performance for historical video data"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "allennlp a deep semantic natural language processing platform"
    - "construction of the literature graph in semantic scholar"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-2 (induced by source paper "what do deep networks like to read"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "interpretable neural predictions with differentiable binary variables"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "analysis methods in neural language processing a survey"
    - "what do deep networks like to see"
    - "swag a large scale adversarial dataset for grounded commonsense inference"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "layerwise relevance visualization in convolutional text graph classifiers"):
  with members:
    - "interpreting and understanding graph convolutional neural network using gradient based attribution methods"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "simplifying graph convolutional networks"
    - "explainability techniques for graph convolutional networks"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-5 (induced by source paper "towards understanding of medical randomized controlled trials by conclusion generation"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "doubletransfer at mediqa 2019 multi source transfer learning for natural language understanding in the medical domain"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "on the automatic generation of medical imaging reports"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "learning to summarize radiology findings"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "generating multi sentence abstractive summaries of interleaved texts"):
  with members:
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "on the automatic generation of medical imaging reports"
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-7 (induced by source paper "claim extraction in biomedical publications using deep discourse model and transfer learning"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "causality extraction based on self attentive bilstm crf with transferred embeddings"
    - "allennlp a deep semantic natural language processing platform"
    - "fever a large scale dataset for fact extraction and verification"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "experiment segmentation in scientific discourse as clause level structured prediction using recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "towards constructing a corpus for studying the effects of treatments and substances reported in pubmed abstracts"):
  with members:
    - "rationalizing neural predictions"
    - "supervised attentions for neural machine translation"
    - "a neural attention model for abstractive sentence summarization"

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)