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 "technical report knn joins using a hybrid approach exploiting cpu gpu workload characteristics"):
  Abstract: This paper studies finding the K nearest neighbors (KNN) of all points in a dataset. Typical solutions to KNN searches use indexing to prune the search, which reduces the number of candidate points that may be within the set of the nearest $K$ points of each query point. In high dimensionality, index searches degrade, making the KNN self-join a prohibitively expensive operation in some scenarios. Furthermore, there are a significant number of distance calculations needed to determine which points are nearest to each query point. To address these challenges, we propose a hybrid CPU/GPU approach. Since the CPU and GPU are considerably different architectures that are best exploited using different algorithms, we advocate for splitting the work between both architectures based on the characteristic workloads defined by the query points in the dataset. As such, we assign dense regions to the GPU, and sparse regions to the CPU to most efficiently exploit the relative strengths of each architecture. Critically, we find that the relative performance gains over the reference implementation across four real-world datasets are a function of the data properties (size, dimensionality, distribution), and number of neighbors, K.

Includes members:
  Member "gpu accelerated self join for the distance similarity metric":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gpu accelerated similarity self join for multi dimensional data")
  Member "fma a dataset for music analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")

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.DC(Distributed, Parallel, and Cluster Computing)