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: "distributed simulation of polychronous and plastic spiking neural networks strong and weak scaling of a representative mini application benchmark executed on a small scale commodity cluster"
  Abstract: We introduce a natively distributed mini-application benchmark representative of plastic spiking neural network simulators. It can be used to measure performances of existing computing platforms and to drive the development of future parallel/distributed computing systems dedicated to the simulation of plastic spiking networks. The mini-application is designed to generate spiking behaviors and synaptic connectivity that do not change when the number of hardware processing nodes is varied, simplifying the quantitative study of scalability on commodity and custom architectures. Here, we present the strong and weak scaling and the profiling of the computational/communication components of the DPSNN-STDP benchmark (Distributed Simulation of Polychronous Spiking Neural Network with synaptic Spike-Timing Dependent Plasticity). In this first test, we used the benchmark to exercise a small-scale cluster of commodity processors (varying the number of used physical cores from 1 to 128). The cluster was interconnected through a commodity network. Bidimensional grids of columns composed of Izhikevich neurons projected synapses locally and toward first, second and third neighboring columns. The size of the simulated network varied from 6.6 Giga synapses down to 200 K synapses. The code demonstrated to be fast and scalable: 10 wall clock seconds were required to simulate one second of activity and plasticity (per Hertz of average firing rate) of a network composed by 3.2 G synapses running on 128 hardware cores clocked @ 2.4 GHz. The mini-application has been designed to be easily interfaced with standard and custom software and hardware communication interfaces. It has been designed from its foundation to be natively distributed and parallel, and should not pose major obstacles against distribution and parallelization on several platforms.

Connected through hyperedge HE-1 (induced by source paper "scaling of a large scale simulation of synchronous slow wave and asynchronous awake like activity of a cortical model with long range interconnections"):
  with members:
    - "real time cortical simulations energy and interconnect scaling on distributed systems"
    - "neuromorphic hardware in the loop training a deep spiking network on the brainscales wafer scale system"

Connected through hyperedge HE-2 (induced by source paper "real time cortical simulations energy and interconnect scaling on distributed systems"):
  with members:
    - "gaussian and exponential lateral connectivity on distributed spiking neural network simulation"

Connected through hyperedge HE-3 (induced by source paper "scaling to 1024 software processes and hardware cores of the distributed simulation of a spiking neural network including up to 20g synapses"):
  with members:
    - "power energy and speed of embedded and server multi cores applied to distributed simulation of spiking neural networks arm in nvidia tegra vs intel xeon quad cores"

Connected through hyperedge HE-4 (induced by source paper "impact of exponential long range and gaussian short range lateral connectivity on the distributed simulation of neural networks including up to 30 billion synapses"):
  with members:
    - "scaling to 1024 software processes and hardware cores of the distributed simulation of a spiking neural network including up to 20g synapses"

Connected through hyperedge HE-5 (induced by source paper "power energy and speed of embedded and server multi cores applied to distributed simulation of spiking neural networks arm in nvidia tegra vs intel xeon quad cores"):
  with members:
    - "euretile 2010 2012 summary first three years of activity of the european reference tiled experiment"

Connected through hyperedge HE-6 (induced by source paper "euretile d7 3 dynamic dal benchmark coding measurements on mpi version of dpsnn stdp distributed plastic spiking neural net and improvements to other dal codes"):
  with members:
    - "euretile 2010 2012 summary first three years of activity of the european reference tiled experiment"

Connected through hyperedge HE-7 (induced by source paper "architectural improvements and 28 nm fpga implementation of the apenet 3d torus network for hybrid hpc systems"):
  with members:
    - "mutual watch dog networking distributed awareness of faults and critical events in petascale exascale 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)