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 "simd x programming and processing of graph algorithms on gpus"):
  Abstract: With high computation power and memory bandwidth, graphics processing units (GPUs) lend themselves to accelerate data-intensive analytics, especially when such applications fit the single instruction multiple data (SIMD) model. However, graph algorithms such as breadth-first search and k-core, often fail to take full advantage of GPUs, due to irregularity in memory access and control flow. To address this challenge, we have developed SIMD-X, for programming and processing of single instruction multiple, complex, data on GPUs. Specifically, the new Active-Compute-Combine (ACC) model not only provides ease of programming to programmers, but more importantly creates opportunities for system-level optimizations. To this end, SIMD-X utilizes just-in-time task management which filters out inactive vertices at runtime and intelligently maps various tasks to different amount of GPU cores in pursuit of workload balancing. In addition, SIMD-X leverages push-pull based kernel fusion that, with the help of a new deadlock-free global barrier, reduces a large number of computation kernels to very few. Using SIMD-X, a user can program a graph algorithm in tens of lines of code, while achieving 3?, 6?, 24?, 3? speedup over Gunrock, Galois, CuSha, and Ligra, respectively.

Includes members:
  Member "maiter an asynchronous graph processing framework for delta based accumulative iterative computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flashgraph processing billion node graphs on an array of commodity ssds")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "gunrock a high performance graph processing library on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hygcn a gcn accelerator with hybrid architecture")
  Member "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "flashgraph processing billion node graphs on an array of commodity ssds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "cudnn efficient primitives for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching for winograd aware quantized networks")
  Member "graphlab a new framework for parallel machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relaxed scheduling for scalable belief propagation")

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)