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: "structural scaffolds for citation intent classification in scientific publications"
  Abstract: Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "citation text generation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "bertscore evaluating text generation with bert"
    - "defending against neural fake news"
    - "gorc a large contextual citation graph of academic papers"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "content based citation recommendation"
    - "hierarchical neural story generation"

Connected through hyperedge HE-2 (induced by source paper "domain independent extraction of scientific concepts from research articles"):
  with members:
    - "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"
    - "deep active learning for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "scibert a pretrained language model for scientific text"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "gorc a large contextual citation graph of academic papers"):
  with members:
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "a context aware citation recommendation model with bert and graph convolutional networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "content based citation recommendation"
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "construction of the literature graph in semantic scholar"
    - "machine learning vs rules and out of the box vs retrained an evaluation of open source bibliographic reference and citation parsers"

Connected through hyperedge HE-5 (induced by source paper "scispacy fast and robust models for biomedical natural language processing"):
  with members:
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "construction of the literature graph in semantic scholar"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

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)