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: "redundancy scheduling in systems with bi modal job service time distribution"
  Abstract: Queuing systems with redundant requests have drawn great attention because of their promise to reduce the job completion time and its variability. Despite a large body of work on this topic, we are still far from fully understanding the benefits of redundancy in practice. We here take one step towards practical systems by studying queuing systems with bi-modal job service time distribution. Such distributions have been observed in practical systems, as can be seen in Google cluster traces. We develop an analogy to a classical urns and balls problem, and use it to study the queuing time performance of two non-adaptive classical scheduling policies: random and round-robin. In particular, we introduce new performance metrics in the analogous model, and argue that they are good indicators of the queuing time performance of non-adaptive scheduling policies. We then propose a new non-adaptive scheduling policy that is based on combinatorial designs, and show that it improves our performance metrics. We observe in simulation that the proposed scheduling policy, as the performance metrics indicate, reduces the queuing times up to 25% compared to random scheduling and up to 100% compared round-robin scheduling.

Connected through hyperedge HE-1 (induced by source paper "load balancing performance in distributed storage with regular balanced redundancy"):
  with members:
    - "service rate region of content access from erasure coded storage"
    - "on the service capacity region of accessing erasure coded content"
    - "optimal combinatorial batch codes based on block designs"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"
    - "memory allocation in distributed storage networks"
    - "balanced allocation on graphs"

Connected through hyperedge HE-2 (induced by source paper "scheduling in the presence of data intensivecompute jobs"):
  with members:
    - "straggler mitigation at scale"
    - "on the effect of task to worker assignment in distributed computing systems with stragglers"
    - "on the delay storage trade off in content download from coded distributed storage 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.PF(Performance)