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: "spectre supporting consumption policies in window based parallel complex event processing"
  Abstract: Distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of complex situations in the surrounding world from basic events like sensor readings. In doing so, DCEP operators detect event patterns on their incoming event streams. To yield high operator throughput, data parallelization frameworks divide the incoming event streams of an operator into overlapping windows that are processed in parallel by a number of operator instances. In doing so, the basic assumption is that the different windows can be processed independently from each other. However, consumption policies enforce that events can only be part of one pattern instance; then, they are consumed, i.e., removed from further pattern detection. That implies that the constituent events of a pattern instance detected in one window are excluded from all other windows as well, which breaks the data parallelism between different windows. In this paper, we tackle this problem by means of speculation: Based on the likelihood of an event's consumption in a window, subsequent windows may speculatively suppress that event. We propose the SPECTRE framework for speculative processing of multiple dependent windows in parallel. Our evaluations show an up to linear scalability of SPECTRE with the number of CPU cores.

Connected through hyperedge HE-1 (induced by source paper "pspice partial match shedding for complex event processing"):
  with members:
    - "espice probabilistic load shedding from input event streams in complex event processing"
    - "on load shedding in complex event processing"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "espice probabilistic load shedding from input event streams in complex event processing"):
  with members:
    - "pspice partial match shedding for complex event processing"
    - "minimizing communication overhead in window based parallel complex event processing"
    - "on load shedding in complex event processing"

Connected through hyperedge HE-4 (induced by source paper "p4cep towards in network complex event processing"):
  with members:
    - "event driven network programming"
    - "snap stateful network wide abstractions for packet 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)