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: "resilience design patterns a structured approach to resilience at extreme scale"
  Abstract: Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires management of various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in HPC systems future systems are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. As a result, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods and metrics to investigate and evaluate resilience holistically in HPC systems that consider impact scope, handling coverage, and performance & power efficiency across the system stack. Additionally, few of the current approaches are portable to newer architectures and software environments that will be deployed on future systems.  In this paper, we develop a structured approach to the management of HPC resilience using the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. Each established solution is described in the form of a pattern that addresses concrete problems in the design of resilient systems. The complete catalog of resilience design patterns provides designers with reusable design elements. We also define a framework that enhances a designer's understanding of the important constraints and opportunities for the design patterns to be implemented and deployed at various layers of the system stack. This design framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also supports optimization of the cost-benefit trade-offs among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types.

Connected through hyperedge HE-1 (induced by source paper "online fault classification in hpc systems through machine learning"):
  with members:
    - "finj a fault injection tool for hpc 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)