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: "don t cry over spilled records memory elasticity of data parallel applications and its application to cluster scheduling"
  Abstract: Understanding the performance of data-parallel workloads when resource-constrained has significant practical importance but unfortunately has received only limited attention. This paper identifies, quantifies and demonstrates memory elasticity, an intrinsic property of data-parallel tasks. Memory elasticity allows tasks to run with significantly less memory that they would ideally want while only paying a moderate performance penalty. For example, we find that given as little as 10% of ideal memory, PageRank and NutchIndexing Hadoop reducers become only 1.2x/1.75x and 1.08x slower. We show that memory elasticity is prevalent in the Hadoop, Spark, Tez and Flink frameworks. We also show that memory elasticity is predictable in nature by building simple models for Hadoop and extending them to Tez and Spark. #R##N#To demonstrate the potential benefits of leveraging memory elasticity, this paper further explores its application to cluster scheduling. In this setting, we observe that the resource vs. time trade-off enabled by memory elasticity becomes a task queuing time vs task runtime trade-off. Tasks may complete faster when scheduled with less memory because their waiting time is reduced. We show that a scheduler can turn this task-level trade-off into improved job completion time and cluster-wide memory utilization. We have integrated memory elasticity into Apache YARN. We show gains of up to 60% in average job completion time on a 50-node Hadoop cluster. Extensive simulations show similar improvements over a large number of scenarios.

Connected through hyperedge HE-1 (induced by source paper "black or white how to develop an autotuner for memory based analytics extended version"):
  with members:
    - "neo a learned query optimizer"
    - "learning based automatic parameter tuning for big data analytics frameworks"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "tempo robust and self tuning resource management in multi tenant parallel databases"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a data driven approach to dynamically adjust resource alocation for compute clusters"):
  with members:
    - "era a framework for economic resource allocation for the cloud"
    - "flexible scheduling of distributed analytic applications"
    - "revisiting size based scheduling with estimated job sizes"
    - "variational gaussian process state space models"
    - "os assisted task preemption for hadoop"
    - "psbs practical size based scheduling"

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)