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: "mutual watch dog networking distributed awareness of faults and critical events in petascale exascale systems"
  Abstract: Many tile systems require techniques to be applied to increase components resilience and control the FIT (Failures In Time) rate. When scaling to peta- exa-scale systems the FIT rate may become unacceptable due to component numerosity, requiring more systemic countermeasures. Thus, the ability to be fault aware, i.e. to detect and collect information about fault and critical events, is a necessary feature that large scale distributed architectures must provide in order to apply systemic fault tolerance techniques. In this context, the LO|FA|MO approach is a way to obtain systemic fault awareness, by implementing a mutual watchdog mechanism and guaranteeing fault detection in a no-single-point-of-failure fashion. This document contains specification and implementation details about this approach, in the shape of a technical report.

Connected through hyperedge HE-1 (induced by source paper "architectural improvements and 28 nm fpga implementation of the apenet 3d torus network for hybrid hpc systems"):
  with members:
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a heterogeneous many core platform for experiments on scalable custom interconnects and management of fault and critical events applied to many process applications vol ii 2012 technical report"):
  with members:
    - "euretile 2010 2012 summary first three years of activity of the european reference tiled experiment"
    - "the distributed network processor a novel off chip and on chip interconnection network architecture"

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)