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: "efficient irregular wavefront propagation algorithms on hybrid cpu gpu machines"
  Abstract: In this paper, we address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively.

Connected through hyperedge HE-1 (induced by source paper "performance analysis and efficient execution on systems with multi core cpus gpus and mics"):
  with members:
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "approximate similarity search for online multimedia services on distributed cpu gpu platforms"

Connected through hyperedge HE-2 (induced by source paper "region templates data representation and management for large scale image analysis"):
  with members:
    - "comparative performance analysis of intel xeon phi gpu and cpu"
    - "approximate similarity search for online multimedia services on distributed cpu gpu platforms"

Connected through hyperedge HE-3 (induced by source paper "scalable locality sensitive hashing for similarity search in high dimensional large scale multimedia datasets"):
  with members:
    - "accelerating nearest neighbor search on manycore systems"
    - "approximate similarity search for online multimedia services on distributed cpu gpu platforms"
    - "searching in one billion vectors re rank with source coding"
    - "entropy based nearest neighbor search in high dimensions"

Connected through hyperedge HE-4 (induced by source paper "comparative performance analysis of intel xeon phi gpu and cpu"):
  with members:
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "approximate similarity search for online multimedia services on distributed cpu gpu platforms"

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)