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: "knowledge infused and consistent complex event processing over real time and persistent streams"
  Abstract: Emerging applications in Internet of Things (IoT) and CyberPhysical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. In particular, we also address temporal consistency issues that arise during fault recovery of query plans that span the boundary between real-time and persistent streams. The proposed -CEPquery model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment. Our results show that we are able to sustain a processing throughput of 3,000 events/secs for -CEPqueries, a 30 improvement over the baseline and sufficient to support a Smart Township, and can resume consistent processing within 20 secs after stream outages as long as 2hours. A semantic CEP model is introduced to query across real-time and persistent streams.The models analytic capability is illustrated using case studies from Smart Grid.Approaches to translate the model into scalable execution are discussed and evaluated.

Connected through hyperedge HE-1 (induced by source paper "a manifesto for future generation cloud computing research directions for the next decade"):
  with members:
    - "cloud benchmarking for maximising performance of scientific applications"
    - "hpc cloud for scientific and business applications taxonomy vision and research challenges"
    - "in datacenter performance analysis of a tensor processing unit"
    - "enorm a framework for edge node resource management"
    - "serverless computing current trends and open problems"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "snafu function as a service faas runtime design and implementation"
    - "what does explainable ai really mean a new conceptualization of perspectives"

Connected through hyperedge HE-2 (induced by source paper "big data and fog computing"):
  with members:
    - "mathbb echo an adaptive orchestration platform for hybrid dataflows across cloud and edge"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "a berkeley view of systems challenges for ai"
    - "the internet of flying things opportunities and challenges with airborne fog computing and mobile cloud in the clouds"
    - "internet of things iot a vision architectural elements and future directions"

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)