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: "column oriented storage techniques for mapreduce"
  Abstract: Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating these techniques to a MapReduce implementation such as Hadoop presents unique challenges that can lead to new design choices. This paper describes how column-oriented storage techniques can be incorporated in Hadoop in a way that preserves its popular programming APIs. #R##N#We show that simply using binary storage formats in Hadoop can provide a 3x performance boost over the naive use of text files. We then introduce a column-oriented storage format that is compatible with the replication and scheduling constraints of Hadoop and show that it can speed up MapReduce jobs on real workloads by an order of magnitude. We also show that dealing with complex column types such as arrays, maps, and nested records, which are common in MapReduce jobs, can incur significant CPU overhead. Finally, we introduce a novel skip list column format and lazy record construction strategy that avoids deserializing unwanted records to provide an additional 1.5x performance boost. Experiments on a real intranet crawl are used to show that our column-oriented storage techniques can improve the performance of the map phase in Hadoop by as much as two orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "typed linear algebra for efficient analytical querying"):
  with members:
    - "typing linear algebra a biproduct oriented approach"

Connected through hyperedge HE-2 (induced by source paper "the family of mapreduce and large scale data processing systems"):
  with members:
    - "distributed graphlab a framework for machine learning in the cloud"
    - "restore reusing results of mapreduce jobs"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "stubby a transformation based optimizer for mapreduce workflows"
    - "automatic optimization for mapreduce programs"
    - "fast clustering using mapreduce"
    - "load balancing for mapreduce based entity resolution"
    - "social content matching in mapreduce"

Connected through hyperedge HE-3 (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:
    - "scaling datalog for machine learning on big data"
    - "only aggressive elephants are fast elephants"
    - "vision paper towards an understanding of the limits of map reduce computation"
    - "fast clustering using mapreduce"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-4 (induced by source paper "only aggressive elephants are fast elephants"):
  with members:
    - "automatic optimization for mapreduce programs"
    - "cophy a scalable portable and interactive index advisor for large workloads"

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)