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 processing of k nearest neighbor joins using mapreduce"
  Abstract: k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operation widely adopted by many data mining applications. As a combination of the k nearest neighbor query and the join operation, kNN join is an expensive operation. Given the increasing volume of data, it is difficult to perform a kNN join on a centralized machine efficiently. In this paper, we investigate how to perform kNN join using MapReduce which is a well-accepted framework for data-intensive applications over clusters of computers. In brief, the mappers cluster objects into groups; the reducers perform the kNN join on each group of objects separately. We design an effective mapping mechanism that exploits pruning rules for distance filtering, and hence reduces both the shuffling and computational costs. To reduce the shuffling cost, we propose two approximate algorithms to minimize the number of replicas. Extensive experiments on our in-house cluster demonstrate that our proposed methods are efficient, robust and scalable.

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 "improving distributed similarity join in metric space with error bounded sampling"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "adaptive sampling for rapidly matching histograms"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "load balancing for mapreduce based entity resolution"

Connected through hyperedge HE-3 (induced by source paper "blaze simplified high performance cluster computing"):
  with members:
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-4 (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 multi way theta join processing using mapreduce"
    - "vision paper towards an understanding of the limits of map reduce computation"

Connected through hyperedge HE-5 (induced by source paper "partitioning indexing and querying spatial data on cloud"):
  with members:
    - "boosting moving object indexing through velocity partitioning"

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)