human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "speeding up the verification phase of set similarity joins in the gpgpu paradigm"):
  Abstract: We investigate the problem of exact set similarity joins using a co-process CPU-GPU scheme. The state-of-the-art CPU solutions split the wok in two main phases. First, filtering and index building takes place to reduce the candidate sets to be compared as much as possible; then the pairs are compared to verify whether they should become part of the result. We investigate in-depth solutions for transferring the second, so-called verification phase, to the GPU addressing several challenges regarding the data serialization and layout, the thread management and the techniques to compare sets of tokens. Using real datasets, we provide concrete experimental proofs that our solutions have reached their maximum potential, since they totally overlap verification with CPU tasks, and manage to yield significant speed-ups, up to 2.6X in our cases.

Includes members:
  Member "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing")
  Member "billion scale similarity search with gpus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "a dynamic hash table for the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "slamcast large scale real time 3d reconstruction and streaming for immersive multi client live telepresence")
  Member "efficient large scale approximate nearest neighbor search on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning")
  Member "a generic inverted index framework for similarity search on the gpu technical report":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cumf_sgd fast and scalable matrix factorization")
  Member "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving distributed similarity join in metric space with error bounded sampling")

Question: Please tell me which class the source paper 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)