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: "content based citation recommendation"
  Abstract: We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and over 22% in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it favors self-citations which are less useful in a citation recommendation setup. We release an online portal (this http URL) for citation recommendation based on our method, and a new dataset OpenCorpus of 7 million research articles to facilitate future research on this task.

Connected through hyperedge HE-1 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation"):
  with members:
    - "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"
    - "passage re ranking with bert"
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "roberta a robustly optimized bert pretraining approach"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "citation text generation"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "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"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "citation recommendation approaches and datasets"):
  with members:
    - "hyperdoc2vec distributed representations of hypertext documents"
    - "core elements in the process of citing publications conceptual overview of the literature"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "machine learning vs rules and out of the box vs retrained an evaluation of open source bibliographic reference and citation parsers"
    - "on measuring bias in online information"
    - "all fingers are not equal intensity of references in scientific articles"
    - "finding news citations for wikipedia"
    - "automated news suggestions for populating wikipedia entity pages"

Connected through hyperedge HE-4 (induced by source paper "hybridcite a hybrid model for context aware citation recommendation"):
  with members:
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "hyperdoc2vec distributed representations of hypertext documents"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "combining aspects of genetic algorithms with weighted recommender hybridization"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "situating sentence embedders with nearest neighbor overlap"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-6 (induced by source paper "gorc a large contextual citation graph of academic papers"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "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"
    - "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-7 (induced by source paper "a scalable hybrid research paper recommender system for microsoft academic"):
  with members:
    - "a web scale system for scientific knowledge exploration"
    - "universal language model fine tuning for text classification"
    - "mr dlib recommendations as a service raas for academia"
    - "a century of science globalization of scientific collaborations citations and innovations"
    - "enriching word vectors with subword information"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "grapal connecting the dots in scientific literature"):
  with members:
    - "a web scale system for scientific knowledge exploration"
    - "construction of the literature graph in semantic scholar"
    - "cl scholar the acl anthology knowledge graph miner"

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)