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: "gunrock gpu graph analytics"
  Abstract: For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.

Connected through hyperedge HE-1 (induced by source paper "optimizing ordered graph algorithms with graphit"):
  with members:
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "distributionally linearizable data structures"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "the gap benchmark suite"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "gpu parallelization of policy iteration rrt"):
  with members:
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "incremental sampling based motion planners using policy iteration methods"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "fast gunrock subgraph matching gsm on gpus"):
  with members:
    - "gsi gpu friendly subgraph isomorphism"
    - "fast bfs based triangle counting on gpus"
    - "a comparative study on exact triangle counting algorithms on the gpu"

Connected through hyperedge HE-4 (induced by source paper "graphblast a high performance linear algebra based graph framework on the gpu"):
  with members:
    - "design principles for sparse matrix multiplication on the gpu"
    - "implementing push pull efficiently in graphblas"
    - "extreme scale de novo metagenome assembly"
    - "junction tree variational autoencoder for molecular graph generation"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "mathematical foundations of the graphblas"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "prioritygraph a unified programming model for optimizing ordered graph algorithms"):
  with members:
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "distributionally linearizable data structures"
    - "gunrock a high performance graph processing library on the gpu"
    - "the gap benchmark suite"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "performance impact of memory channels on sparse and irregular algorithms"):
  with members:
    - "programming strategies for irregular algorithms on the emu chick"
    - "exploring the performance benefit of hybrid memory system on hpc environments"
    - "mathematical foundations of the graphblas"
    - "the gap benchmark suite"

Connected through hyperedge HE-7 (induced by source paper "message scheduling for performant many core belief propagation"):
  with members:
    - "graph partition neural networks for semi supervised classification"
    - "loopy belief propagation for approximate inference an empirical study"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-8 (induced by source paper "an adaptive load balancer for graph analytical applications on gpus"):
  with members:
    - "dynamic load balancing strategies for graph applications on gpus"
    - "multi gpu graph analytics"
    - "gunrock a high performance graph processing library on the gpu"
    - "parallel breadth first search on distributed memory 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)