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: "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"
  Abstract: Energy disaggregation or non-intrusive load monitoring addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem, where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper, an approach based on  Sum-to-k constrained non-negative matrix factorization  (S2K-NMF) is proposed. By imposing the sum-to- k  constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home; and heating, ventilating, and air conditioning components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory. Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms.

Connected through hyperedge HE-1 (induced by source paper "unsupervised algorithm for disaggregating low sampling rate electricity consumption of households"):
  with members:
    - "review of smart meter data analytics applications methodologies and challenges"
    - "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-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"
    - "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"
    - "a comparison of non intrusive load monitoring methods for commercial and residential buildings"

Connected through hyperedge HE-3 (induced by source paper "person re identification using visual attention"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "spatial random sampling a structure preserving data sketching tool"
    - "person re identification by deep joint learning of multi loss classification"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "gated siamese convolutional neural network architecture for human re identification"

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.CE(Computational Engineering, Finance, and Science)