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: "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
  Abstract: We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the `PICO' elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.

Connected through hyperedge HE-1 (induced by source paper "data mining in clinical trial text transformers for classification and question answering tasks"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "huggingface s transformers state of the art natural language processing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "squad 100 000 questions for machine comprehension of text"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "scibert a pretrained language model for scientific text"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"

Connected through hyperedge HE-3 (induced by source paper "message distortion in information cascades"):
  with members:
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "fake news detection in social networks via crowd signals"
    - "online human bot interactions detection estimation and characterization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "information evolution in social networks"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-4 (induced by source paper "predicting annotation difficulty to improve task routing and model performance for biomedical information extraction"):
  with members:
    - "crowdsourcing ground truth for medical relation extraction"
    - "syntactic patterns improve information extraction for medical search"
    - "attention is all you need"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "a study on agreement in pico span annotations"):
  with members:
    - "yedda a lightweight collaborative text span annotation tool"
    - "neural architectures for named entity recognition"

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)