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: "holistic measures for evaluating prediction models in smart grids"
  Abstract: The performance of prediction models is often based on “abstract metrics” that estimate the model’s ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging “big data” domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models’ behavior and their impact on real applications, which benefit both data mining researchers and practitioners.

Connected through hyperedge HE-1 (induced by source paper "characterizing application scheduling on edge fog and cloud computing resources"):
  with members:
    - "violet a large scale virtual environment for internet of things"
    - "adaptive energy aware scheduling of dynamic event analytics across edge and cloud resources"
    - "slaq quality driven scheduling for distributed machine learning"
    - "arm wrestling with big data a study of commodity arm64 server for big data workloads"
    - "a manifesto for future generation cloud computing research directions for the next decade"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "big data and fog computing"

Connected through hyperedge HE-2 (induced by source paper "characterizing iot data and its quality for use"):
  with members:
    - "towards a data driven iot software architecture for smart city utilities"
    - "a review of privacy and consent management in healthcare a focus on emerging data sources"
    - "a trust model for data sharing in smart cities"
    - "when things matter a data centric view of the internet of things"

Connected through hyperedge HE-3 (induced by source paper "digital technologies in airport ground operations"):
  with members:
    - "large scale correlation clustering optimization"

Connected through hyperedge HE-4 (induced by source paper "demystifying fog computing characterizing architectures applications and abstractions"):
  with members:
    - "distributed scheduling of event analytics across edge and cloud"
    - "fog computing principles architectures and applications"
    - "fog computing focusing on mobile users at the edge"
    - "the internet of flying things opportunities and challenges with airborne fog computing and mobile cloud in the clouds"
    - "towards a practical architecture for india centric internet of things"

Connected through hyperedge HE-5 (induced by source paper "collaborative reuse of streaming dataflows in iot applications"):
  with members:
    - "mathbb echo an adaptive orchestration platform for hybrid dataflows across cloud and edge"
    - "riotbench a real time iot benchmark for distributed stream processing platforms"
    - "sensing as a service model for smart cities supported by internet of things"

Connected through hyperedge HE-6 (induced by source paper "benchmarking distributed stream processing platforms for iot applications"):
  with members:
    - "bigdatabench a big data benchmark suite from web search 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.LG(Machine Learning)