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: "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
  Abstract: This work proposes V-SMART-Join, a scalable MapReduce-based framework for discovering all pairs of similar entities. The V-SMART-Join framework is applicable to sets, multisets, and vectors. V-SMART-Join is motivated by the observed skew in the underlying distributions of Internet traffic, and is a family of 2-stage algorithms, where the first stage computes and joins the partial results, and the second stage computes the similarity exactly for all candidate pairs. The V-SMART-Join algorithms are very efficient and scalable in the number of entities, as well as their cardinalities. They were up to 30 times faster than the state of the art algorithm, VCL, when compared on a real dataset of a small size. We also established the scalability of the proposed algorithms by running them on a dataset of a realistic size, on which VCL never succeeded to finish. Experiments were run using real datasets of IPs and cookies, where each IP is represented as a multiset of cookies, and the goal is to discover similar IPs to identify Internet proxies.

Connected through hyperedge HE-1 (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"
    - "efficient processing of k nearest neighbor joins using mapreduce"
    - "load balancing for mapreduce based entity resolution"

Connected through hyperedge HE-2 (induced by source paper "scalable similarity joins of tokenized strings"):
  with members:
    - "paying for likes understanding facebook like fraud using honeypots"
    - "pass join a partition based method for similarity joins"
    - "uncovering social network sybils in the wild"
    - "benchmarking declarative approximate selection predicates"

Connected through hyperedge HE-3 (induced by source paper "speeding up the verification phase of set similarity joins in the gpgpu paradigm"):
  with members:
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "billion scale similarity search with gpus"
    - "a dynamic hash table for the gpu"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "a generic inverted index framework for similarity search on the gpu technical report"

Connected through hyperedge HE-4 (induced by source paper "preference driven similarity join"):
  with members:
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-5 (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"
    - "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"
    - "column oriented storage techniques for mapreduce"

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)