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: "scispacy fast and robust models for biomedical natural language processing"
  Abstract: Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new tool for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records"):
  with members:
    - "secnlp a survey of embeddings in clinical natural language processing"
    - "bpe dropout simple and effective subword regularization"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "named entity recognition in electronic health records using transfer learning bootstrapped neural networks"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "not enough data deep learning to the rescue"
    - "huggingface s transformers state of the art natural language processing"

Connected through hyperedge HE-2 (induced by source paper "cbag conditional biomedical abstract generation"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "language models as knowledge bases"
    - "the deepfake detection challenge dfdc preview dataset"
    - "ernie enhanced representation through knowledge integration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-3 (induced by source paper "annotating and extracting synthesis process of all solid state batteries from scientific literature"):
  with members:
    - "the materials science procedural text corpus annotating materials synthesis procedures with shallow semantic structures"
    - "scibert a pretrained language model for scientific text"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "playing by the book an interactive game approach for action graph extraction from text"
    - "automatically extracting action graphs from materials science synthesis procedures"

Connected through hyperedge HE-4 (induced by source paper "agatha automatic graph mining and transformer based hypothesis generation approach"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "pytorch biggraph a large scale graph embedding system"
    - "large scale validation of hypothesis generation systems via candidate ranking"
    - "are abstracts enough for hypothesis generation"
    - "attention is all you need"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-5 (induced by source paper "understanding patient complaint characteristics using contextual clinical bert embeddings"):
  with members:
    - "comprehend medical a named entity recognition and relationship extraction web service"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "attention is not not explanation"
    - "on identifiability in transformers"
    - "inferring which medical treatments work from reports of clinical trials"
    - "fine grained sentiment analysis with faithful attention"
    - "on the validity of self attention as explanation in transformer models"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "is attention interpretable"

Connected through hyperedge HE-7 (induced by source paper "reflex flexible framework for relation extraction in multiple domains"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "publicly available clinical bert embeddings"
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"
    - "a hybrid deep learning approach for medical relation extraction"

Connected through hyperedge HE-8 (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"
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"

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)