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 asilomar report on database research"
  Abstract: The database research community is rightly proud of success in basic research, and its remarkable record of technology transfer. Now the field needs to radically broaden its research focus to attack the issues of capturing, storing, analyzing, and presenting the vast array of online data. The database research community should embrace a broader research agenda -- broadening the definition of database management to embrace all the content of the Web and other online data stores, and rethinking our fundamental assumptions in light of technology shifts. To accelerate this transition, we recommend changing the way research results are evaluated and presented. In particular, we advocate encouraging more speculative and long-range work, moving conferences to a poster format, and publishing all research literature on the Web.

Connected through hyperedge HE-1 (induced by source paper "classytune a performance auto tuner for systems in the cloud"):
  with members:
    - "large scale evolution of image classifiers"
    - "using bad learners to find good configurations"
    - "acts in need automatic configuration tuning with scalability guarantees"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "an overview on clustering methods"
    - "learning to order things"

Connected through hyperedge HE-2 (induced by source paper "transfer learning with bellwethers to find good configurations"):
  with members:
    - "finding faster configurations using flash"
    - "scout an experienced guide to find the best cloud configuration"
    - "low level augmented bayesian optimization for finding the best cloud vm"
    - "bellwethers a baseline method for transfer learning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "bestconfig tapping the performance potential of systems via automatic configuration tuning"):
  with members:
    - "acts in need automatic configuration tuning with scalability guarantees"
    - "aloja ml a framework for automating characterization and knowledge discovery in hadoop deployments"
    - "bigop generating comprehensive big data workloads as a benchmarking framework"

Connected through hyperedge HE-4 (induced by source paper "acts in need automatic configuration tuning with scalability guarantees"):
  with members:
    - "aloja ml a framework for automating characterization and knowledge discovery in hadoop deployments"

Connected through hyperedge HE-5 (induced by source paper "naughton s wisconsin bibliography a brief guide"):
  with members:
    - "revisiting differentially private regression lessons from learning theory and their consequences"
    - "on load shedding in complex event processing"
    - "the case for a structured approach to managing unstructured data"
    - "the lowell database research self assessment"

Connected through hyperedge HE-6 (induced by source paper "a call to arms revisiting database design"):
  with members:
    - "building on quicksand"
    - "the lowell database research self assessment"

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.DB(Databases)