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: "scaling datalog for machine learning on big data"
  Abstract: In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine learning task, or hardcoding new optimizations, we argue for the use of recursive queries to program a variety of machine learning systems. By taking this approach, database query optimization techniques can be utilized to identify effective execution plans, and the resulting runtime plans can be executed on a single unified data-parallel query processing engine. As a proof of concept, we consider two programming models--Pregel and Iterative Map-Reduce-Update---from the machine learning domain, and show how they can be captured in Datalog, tuned for a specific task, and then compiled into an optimized physical plan. Experiments performed on a large computing cluster with real data demonstrate that this declarative approach can provide very good performance while offering both increased generality and programming ease.

Connected through hyperedge HE-1 (induced by source paper "scaling up reasoning and advanced analytics on bigdata"):
  with members:
    - "vicious circle principle and logic programs with aggregates"
    - "clingo asp control preliminary report"
    - "efficient computation of the well founded semantics over big data"
    - "multi core processors an overview"
    - "relational transducers for declarative networking"
    - "xsb extending prolog with tabled logic programming"
    - "the dlv system for knowledge representation and reasoning"
    - "well founded and stable semantics of logic programs with aggregates"

Connected through hyperedge HE-2 (induced by source paper "pregelix big ger graph analytics on a dataflow engine"):
  with members:
    - "distributed graphlab a framework for machine learning in the cloud"
    - "rex recursive delta based data centric computation"
    - "spinning fast iterative data flows"

Connected through hyperedge HE-3 (induced by source paper "asterixdb a scalable open source bdms"):
  with members:
    - "scalable fault tolerant data feeds in asterixdb"

Connected through hyperedge HE-4 (induced by source paper "mli an api for distributed machine learning"):
  with members:
    - "mlpack a scalable c machine learning library"
    - "the madlib analytics library or mad skills the sql"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-5 (induced by source paper "mapreduce is good enough if all you have is a hammer throw away everything that s not a nail"):
  with members:
    - "only aggressive elephants are fast elephants"
    - "vision paper towards an understanding of the limits of map reduce computation"
    - "fast clustering using mapreduce"
    - "column oriented storage techniques for mapreduce"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-6 (induced by source paper "spinning fast iterative data flows"):
  with members:
    - "opening the black boxes in data flow optimization"
    - "graphlab a new framework for parallel machine learning"

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)