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: "nephele streaming stream processing under qos constraints at scale"
  Abstract: The ability to process large numbers of continuous data streams in a near-real-time fashion has become a crucial prerequisite for many scientific and industrial use cases in recent years. While the individual data streams are usually trivial to process, their aggregated data volumes easily exceed the scalability of traditional stream processing systems.#R##N##R##N#At the same time, massively-parallel data processing systems like MapReduce or Dryad currently enjoy a tremendous popularity for data-intensive applications and have proven to scale to large numbers of nodes. Many of these systems also provide streaming capabilities. However, unlike traditional stream processors, these systems have disregarded QoS requirements of prospective stream processing applications so far.#R##N##R##N#In this paper we address this gap. First, we analyze common design principles of today's parallel data processing frameworks and identify those principles that provide degrees of freedom in trading off the QoS goals latency and throughput. Second, we propose a highly distributed scheme which allows these frameworks to detect violations of user-defined QoS constraints and optimize the job execution without manual interaction. As a proof of concept, we implemented our approach for our massively-parallel data processing framework Nephele and evaluated its effectiveness through a comparison with Hadoop Online.#R##N##R##N#For an example streaming application from the multimedia domain running on a cluster of 200 nodes, our approach improves the processing latency by a factor of at least 13 while preserving high data throughput when needed.

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"
    - "toward reliable and rapid elasticity for streaming dataflows on clouds"
    - "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"

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)