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: "multi gpu graph analytics"
  Abstract: We present a single-node, multi-GPU programmable graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graphs with billions of edges. Directly using the single-GPU implementations, our design only requires programmers to specify a few algorithm-dependent concerns, hiding most multi-GPU related implementation details. We analyze the theoretical and practical limits to scalability in the context of varying graph primitives and datasets. We describe several optimizations, such as direction optimizing traversal, and a just-enough memory allocation scheme, for better performance and smaller memory consumption. Compared to previous work, we achieve best-of-class performance across operations and datasets, including excellent strong and weak scalability on most primitives as we increase the number of GPUs in the system.

Connected through hyperedge HE-1 (induced by source paper "an adaptive load balancer for graph analytical applications on gpus"):
  with members:
    - "gunrock gpu graph analytics"
    - "dynamic load balancing strategies for graph applications on gpus"
    - "gunrock a high performance graph processing library on the gpu"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-2 (induced by source paper "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"):
  with members:
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "a performance comparison of sort and scan libraries for gpus"
    - "gunrock a high performance graph processing library on the gpu"

Connected through hyperedge HE-3 (induced by source paper "gunrock gpu graph analytics"):
  with members:
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "snap a general purpose network analysis and graph mining library"
    - "counting triangles in large graphs on gpu"
    - "mathematical foundations of the graphblas"
    - "accelerating direction optimized breadth first search on hybrid architectures"
    - "emptyheaded boolean algebra based graph processing"
    - "gunrock a high performance graph processing library on the gpu"
    - "efficient large scale graph processing on hybrid cpu and gpu systems"

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)