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 scalable hybrid research paper recommender system for microsoft academic"
  Abstract: We present the design and methodology for the large scale hybrid paper recommender system used by Microsoft Academic. The system provides recommendations for approximately 160 million English research papers and patents. Our approach handles incomplete citation information while also alleviating the cold-start problem that often affects other recommender systems. We use the Microsoft Academic Graph (MAG), titles, and available abstracts of research papers to build a recommendation list for all documents, thereby combining co-citation and content based approaches. Tuning system parameters also allows for blending and prioritization of each approach which, in turn, allows us to balance paper novelty versus authority in recommendation results. We evaluate the generated recommendations via a user study of 40 participants, with over 2400 recommendation pairs graded and discuss the quality of the results using P@10 and nDCG scores. We see that there is a strong correlation between participant scores and the similarity rankings produced by our system but that additional focus needs to be put towards improving recommender precision, particularly for content based recommendations. The results of the user survey and associated analysis scripts are made available via GitHub and the recommendations produced by our system are available as part of the MAG on Azure to facilitate further research and light up novel research paper recommendation applications.

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"
    - "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"
    - "content based citation recommendation"

Connected through hyperedge HE-2 (induced by source paper "explanation based tuning of opaque machine learners with application to paper recommendation"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "the challenge of crafting intelligible intelligence"
    - "explainable recommendation a survey and new perspectives"
    - "construction of the literature graph in semantic scholar"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "machine teaching a new paradigm for building machine learning systems"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "hybridcite a hybrid model for context aware citation recommendation"):
  with members:
    - "content based citation recommendation"
    - "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-4 (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 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"

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.DL(Digital Libraries)