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: "efficient multi way theta join processing using mapreduce"
  Abstract: Multi-way Theta-join queries are powerful in describing complex relations and therefore widely employed in real practices. However, existing solutions from traditional distributed and parallel databases for multi-way Theta-join queries cannot be easily extended to fit a shared-nothing distributed computing paradigm, which is proven to be able to support OLAP applications over immense data volumes. In this work, we study the problem of efficient processing of multi-way Theta-join queries using MapReduce from a cost-effective perspective. Although there have been some works using the (key,value) pair-based programming model to support join operations, efficient processing of multi-way Theta-join queries has never been fully explored. The substantial challenge lies in, given a number of processing units (that can run Map or Reduce tasks), mapping a multi-way Theta-join query to a number of MapReduce jobs and having them executed in a well scheduled sequence, such that the total processing time span is minimized. Our solution mainly includes two parts: 1) cost metrics for both single MapReduce job and a number of MapReduce jobs executed in a certain order; 2) the efficient execution of a chain-typed Theta-join with only one MapReduce job. Comparing with the query evaluation strategy proposed in [23] and the widely adopted Pig Latin and Hive SQL solutions, our method achieves significant improvement of the join processing efficiency.

Connected through hyperedge HE-1 (induced by source paper "a survey on geographically distributed big data processing using mapreduce"):
  with members:
    - "security and privacy aspects in mapreduce on clouds a survey"
    - "towards geo distributed machine learning"
    - "the bigdawg polystore system and architecture"
    - "tensorflow a system for large scale machine learning"
    - "medusa an efficient cloud fault tolerant mapreduce"
    - "mllib machine learning in apache spark"
    - "assignment problems of different sized inputs in mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"

Connected through hyperedge HE-2 (induced by source paper "gpu based efficient join algorithms on hadoop"):
  with members:
    - "sharesskew an algorithm to handle skew for joins in mapreduce"
    - "revisiting co processing for hash joins on the coupled cpu gpu architecture"
    - "fast updates on read optimized databases using multi core cpus"

Connected through hyperedge HE-3 (induced by source paper "security and privacy aspects in mapreduce on clouds a survey"):
  with members:
    - "assignment problems of different sized inputs in mapreduce"
    - "computing marginals using mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"
    - "on the computational complexity of mapreduce"
    - "the family of mapreduce and large scale data processing systems"
    - "enumerating subgraph instances using map reduce"
    - "efficient processing of k nearest neighbor joins using mapreduce"
    - "vision paper towards an understanding of the limits of map reduce computation"

Connected through hyperedge HE-4 (induced by source paper "sharesskew an algorithm to handle skew for joins in mapreduce"):
  with members:
    - "worst case optimal algorithms for parallel query processing"
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "α k minimal sorting and skew join in mpi and mapreduce"
    - "skew in parallel query processing"
    - "gym a multiround join algorithm in mapreduce"
    - "the family of mapreduce and large scale data processing systems"
    - "beyond worst case analysis for joins with minesweeper"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-5 (induced by source paper "dualtable a hybrid storage model for update optimization in hive"):
  with members:
    - "dgfindex for smart grid enhancing hive with a cost effective multidimensional range index"
    - "stubby a transformation based optimizer for mapreduce workflows"

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)