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: "toward reliable and rapid elasticity for streaming dataflows on clouds"
  Abstract: The pervasive availability of streaming data is driving Fast Data platforms for low-latency streaming applications. Such applications need to respond to dynamism in the input rates and task behavior using scale-in and -out on elastic Cloud resources. Platforms like Apache Storm do not provide robust means to respond to such dynamism and for rapid task migration across VMs. We propose several dataflow checkpoint and migration approaches that allow a running streaming dataflow to migrate, without any loss of in-flight messages or their internal tasks states, while reducing the time to recover and stabilize. We implement these strategies on Storm and evaluate them using micro and application dataflows for scaling in and out on 2 – 21 Cloud VMs. Our results show that we can migrate large dataflows and catchup with their processing 75% faster than Storm, which takes over 140 secs. We also find that our approaches stabilize the application up to 42% faster, and there is no failure and re-processing of messages.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive survey on parallelization and elasticity in stream processing"):
  with members:
    - "p4cep towards in network complex event processing"
    - "minimizing communication overhead in window based parallel complex event processing"
    - "event forecasting with pattern markov chains"
    - "distributed data stream processing and edge computing a survey on resource elasticity and future directions"
    - "spectre supporting consumption policies in window based parallel complex event processing"
    - "integrative dynamic reconfiguration in a parallel stream processing engine"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "the power of both choices practical load balancing for distributed stream processing engines"

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)