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: "likwid monitoring stack a flexible framework enabling job specific performance monitoring for the masses"
  Abstract: System monitoring is an established tool to measure the utilization and health of HPC systems. Usually system monitoring infrastructures make no connection to job information and do not utilize hardware performance monitoring (HPM) data. To increase the efficient use of HPC systems automatic and continuous performance monitoring of jobs is an essential component. It can help to identify pathological cases, provides instant performance feedback to the users, offers initial data to judge on the optimization potential of applications and helps to build a statistical foundation about application specific system usage. The LIKWID monitoring stack is a modular framework build on top of the LIKWID tools library. It aims on enabling job specific performance monitoring using HPM data, system metrics and applicationlevel data for small to medium sized commodity clusters. Moreover, it is designed to integrate in existing monitoring infrastructures to speed up the change from pure system monitoring to job-aware monitoring.

Connected through hyperedge HE-1 (induced by source paper "mpcdf hpc performance monitoring system enabling insight via job specific analysis"):
  with members:
    - "online anomaly detection in hpc systems"
    - "big data meets hpc log analytics scalable approach to understanding systems at extreme scale"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"

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)