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: "gpu accelerated self join for the distance similarity metric"
  Abstract: The self-join finds all objects in a dataset within a threshold of each other defined by a similarity metric. As such, the self-join is a building block for the field of databases and data mining, and is employed in Big Data applications. In this paper, we advance a GPU-efficient algorithm for the similarity self-join that uses the Euclidean distance metric. The search-and-refine strategy is an efficient approach for low dimensionality datasets, as index searches degrade with increasing dimension (i.e., the curse of dimensionality). Thus, we target the low dimensionality problem, and compare our GPU self-join to a search-and-refine implementation, and a state-of-the-art parallel algorithm. In low dimensionality, there are several unique challenges associated with efficiently solving the self-join problem on the GPU. Low dimensional data often results in higher data densities, causing a significant number of distance calculations and a large result set. As dimensionality increases, index searches become increasingly exhaustive, forming a performance bottleneck. We advance several techniques to overcome these challenges using the GPU. The techniques we propose include a GPU-efficient index that employs a bounded search, a batching scheme to accommodate large result set sizes, and a reduction in distance calculations through duplicate search removal. Our GPU self-join outperforms both search-and-refine and state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "technical report knn joins using a hybrid approach exploiting cpu gpu workload characteristics"):
  with members:
    - "fma a dataset for music analysis"

Connected through hyperedge HE-2 (induced by source paper "gpu accelerated similarity self join for multi dimensional data"):
  with members:
    - "curse of dimensionality in pivot based indexes"

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.DC(Distributed, Parallel, and Cluster Computing)