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: "a comparison of non intrusive load monitoring methods for commercial and residential buildings"
  Abstract: Non intrusive load monitoring (NILM), or energy disaggregation, is the process of separating the total electricity consumption of a building as measured at single point into the building’s constituent loads. Previous research in the eld has mostly focused on residential buildings, and although the potential benets of applying this technology to commercial buildings have been recognised since the eld’s conception, NILM in the commercial domain has been largely unexplored by the academic community. As a result of the heterogeneity of this section of the building stock (i.e., encompassing buildings as diverse as airports, malls and coee shops), and hence the loads within them, many of the solutions developed for residential energy disaggregation do not apply directly. In this paper we highlight some insights for NILM in the commercial domain using data collected from a large smart meter deployment within an educational campus in Delhi, India, of which a subset of the data has been released for public use. We present an empirical characterisation of loads in commercial buildings, highlighting the dierences in energy consumption and load characteristics between residential and commercial buildings. We assess the validity of the assumptions generally made by NILM solutions for residential buildings when applied to measurements from commercial facilities. Based on our observations, we discuss the required traits for a NILM system for commercial buildings, and run benchmark residential NILM algorithms on our data set to conrm our observations. To advance the research in commercial buildings energy disaggregation, we release a subset of our data set, called COMBED (commercial building energy data set).

Connected through hyperedge HE-1 (induced by source paper "on the feasibility of generic deep disaggregation for single load extraction"):
  with members:
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "a generative model for non intrusive load monitoring in commercial buildings"):
  with members:
    - "rae the rainforest automation energy dataset for smart grid meter data analysis"
    - "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"
    - "cooll controlled on off loads library a public dataset of high sampled electrical signals for appliance identification"
    - "non intrusive load monitoring a review and outlook"

Connected through hyperedge HE-3 (induced by source paper "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"):
  with members:
    - "greend an energy consumption dataset of households in italy and austria"
    - "metadata for energy disaggregation"

Connected through hyperedge HE-4 (induced by source paper "nilmtk v0 2 a non intrusive load monitoring toolkit for large scale data sets demo abstract"):
  with members:
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "greend an energy consumption dataset of households in italy and austria"
    - "metadata for energy disaggregation"

Connected through hyperedge HE-5 (induced by source paper "data driven energy efficiency in buildings"):
  with members:
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "greend an energy consumption dataset of households in italy and austria"

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.SY(Systems and Control)