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: "performance characterization of multi threaded graph processing applications on many integrated core architecture"
  Abstract: In the age of Big Data, parallel graph processing has been a critical technique to analyze and understand connected data. Meanwhile, Moore's Law continues by integrating more cores into a single chip in the deep-nano regime. Many-Integrated-Core (MIC) processors emerge as a promising solution to process large graphs. In this paper, we empirically evaluate various computing platforms including an Intel Xeon E5 CPU, an Nvidia Tesla P40 GPU and a Xeon Phi 7210 MIC processor codenamed Knights Landing (KNL) in the domain of parallel graph processing. We show that the KNL gains encouraging performance and power efficiency when processing graphs, so that it can become an auspicious alternative to traditional CPUs and GPUs. We further characterize the impact of KNL architectural enhancements on the performance of a state-of-the-art graph framework. We have four key observations: 1 Different graph applications require distinctive numbers of threads to reach the peak performance. For the same application, various datasets need even different numbers of threads to achieve the best performance. 2 Not all graph applications actually benefit from high bandwidth MCDRAMs, while some of them favor low latency DDR4 DRAMs. 3 Vector processing units executing AVX512 SIMD instructions on KNLs are underutilized when running the state-of-the-art graph framework. 4 The sub-NUMA cache clustering mode offering the lowest local memory access latency hurts the performance of graph benchmarks that are lack of NUMA awareness. At last, we suggest future works including system auto-tuning tools and graph framework optimizations to fully exploit the potential of KNL for parallel graph processing.

Connected through hyperedge HE-1 (induced by source paper "a survey on agent based simulation using hardware accelerators"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "review on microscopic pedestrian simulation model"
    - "epiphany v a 1024 processor 64 bit risc system on chip"
    - "ecolab agent based modeling for c programmers"
    - "parallel implementation of the transims micro simulation"

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)