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: "the sdss skyserver public access to the sloan digital sky server data"
  Abstract: The SkyServer provides Internet access to the public Sloan Digi-tal Sky Survey (SDSS) data for both astronomers and for science education. This paper describes the SkyServer goals and archi-tecture. It also describes our experience operating the SkyServer on the Internet. The SDSS data is public and well-documented so it makes a good test platform for research on database algorithms and performance.

Connected through hyperedge HE-1 (induced by source paper "facilitating sql query composition and analysis"):
  with members:
    - "query2vec nlp meets databases for generalized workload analytics"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a survey of machine learning for big code and naturalness"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "convolutional neural networks for text categorization shallow word level vs deep character level"
    - "very deep convolutional networks for text classification"
    - "database meets deep learning challenges and opportunities"

Connected through hyperedge HE-2 (induced by source paper "a comparative survey of recent natural language interfaces for databases"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "amuse multilingual semantic parsing for question answering over linked data"
    - "knowledge rich natural language queries over structured biological databases"
    - "learning a neural semantic parser from user feedback"
    - "an analytical study of large sparql query logs"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "language to logical form with neural attention"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "explaining aggregates for exploratory analytics"):
  with members:
    - "foresight recommending visual insights"
    - "axiomatic attribution for deep networks"
    - "database learning toward a database that becomes smarter every time"
    - "macrobase prioritizing attention in fast data"
    - "macrobase analytic monitoring for the internet of things"
    - "why should i trust you explaining the predictions of any classifier"
    - "qfix diagnosing errors through query histories"
    - "perfxplain debugging mapreduce job performance"

Connected through hyperedge HE-4 (induced by source paper "a dynamic data middleware cache for rapidly growing scientific repositories"):
  with members:
    - "skyserver traffic report the first five years"
    - "cdn content distribution network"

Connected through hyperedge HE-5 (induced by source paper "liferaft data driven batch processing for the exploration of scientific databases"):
  with members:
    - "the zones algorithm for finding points near a point or cross matching spatial datasets"
    - "batch is back casjobs serving multi tb data on the web"
    - "where the rubber meets the sky bridging the gap between databases and science"

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)