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: "review of smart meter data analytics applications methodologies and challenges"
  Abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Connected through hyperedge HE-1 (induced by source paper "learning optimal solutions for extremely fast ac optimal power flow"):
  with members:
    - "learning for dc opf classifying active sets using neural nets"
    - "deepopf deep neural network for dc optimal power flow"
    - "deep neural network approximation theory"
    - "machine learning for ac optimal power flow"
    - "machine learning in the air"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "unsupervised algorithm for disaggregating low sampling rate electricity consumption of households"):
  with members:
    - "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "gspbox a toolbox for signal processing on graphs"
    - "non negative sparse coding"

Connected through hyperedge HE-3 (induced by source paper "a survey on state estimation techniques and challenges in smart distribution systems"):
  with members:
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "robust recovery of missing data in electricity distribution systems"
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"
    - "topology detection in microgrids with micro synchrophasors"

Connected through hyperedge HE-4 (induced by source paper "predicting electricity consumption using deep recurrent neural networks"):
  with members:
    - "language modeling through long term memory network"
    - "dynamic routing between capsules"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-5 (induced by source paper "a data driven framework for assessing cold load pick up demand in service restoration"):
  with members:
    - "a survey on state estimation techniques and challenges in smart distribution systems"

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.CY(Computers and Society)