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: "on load shedding in complex event processing"
  Abstract: Complex Event Processing (CEP) is a stream processing model that focuses on detecting event patterns in continuous event streams. While the CEP model has gained popularity in the research communities and commercial technologies, the problem of gracefully degrading performance under heavy load in the presence of resource constraints, or load shedding, has been largely overlooked. CEP is similar to "classical" stream data management, but addresses a substantially different class of queries. This unfortunately renders the load shedding algorithms developed for stream data processing inapplicable. In this paper we study CEP load shedding under various resource constraints. We formalize broad classes of CEP load-shedding scenarios as different optimization problems. We demonstrate an array of complexity results that reveal the hardness of these problems and construct shedding algorithms with performance guarantees. Our results shed some light on the difficulty of developing load-shedding algorithms that maximize utility.

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"
    - "spectre supporting consumption policies in window based parallel complex event processing"

Connected through hyperedge HE-2 (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"
    - "spectre supporting consumption policies in window based parallel complex event processing"

Connected through hyperedge HE-3 (induced by source paper "foundations of complex event processing"):
  with members:
    - "consistent streaming through time a vision for event stream processing"

Connected through hyperedge HE-4 (induced by source paper "naughton s wisconsin bibliography a brief guide"):
  with members:
    - "revisiting differentially private regression lessons from learning theory and their consequences"
    - "the case for a structured approach to managing unstructured data"
    - "the lowell database research self assessment"
    - "the asilomar report on database research"

Connected through hyperedge HE-5 (induced by source paper "when things matter a data centric view of the internet of things"):
  with members:
    - "context aware computing for the internet of things a survey"
    - "the hidden web xml and semantic web a scientific data management perspective"
    - "distributed inference and query processing for rfid tracking and monitoring"

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.DB(Databases)