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: "an empirical investigation of v i trajectory based load signatures for non intrusive load monitoring"
  Abstract: Choice of load signature or feature space is one of the most fundamental design choices for non-intrusive load monitoring or energy disaggregation problem. Electrical power quantities, harmonic load characteristics, canonical transient and steady-state waveforms are some of the typical choices of load signature or load signature basis for current research addressing appliance classification and prediction. This paper expands and evaluates appliance load signatures based on V-I trajectory-the mutual locus of instantaneous voltage and current waveforms-for precision and robustness of prediction in classification algorithms used to disaggregate residential overall energy use and predict constituent appliance profiles. We also demonstrate the use of variants of differential evolution as a novel strategy for selection of optimal load models in context of energy disaggregation. A publicly available benchmark dataset REDD is employed for evaluation purposes. Our experimental evaluations indicate that these load signatures, in conjunction with a number of popular classification algorithms, offer better or generally comparable overall precision of prediction, robustness and reliability against dynamic, noisy and highly similar load signatures with reference to electrical power quantities and harmonic content. Herein, wave-shape features are found to be an effective new basis of classification and prediction for semi-automated energy disaggregation and monitoring.

Connected through hyperedge HE-1 (induced by source paper "neural network ensembles to real time identification of plug level appliance measurements"):
  with members:
    - "neural nilm deep neural networks applied to energy disaggregation"

Connected through hyperedge HE-2 (induced by source paper "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"):
  with members:
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "innovation pursuit a new approach to subspace clustering"
    - "feature encoding in band limited distributed surveillance systems"
    - "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
    - "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-3 (induced by source paper "non intrusive load monitoring a review and outlook"):
  with members:
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "greend an energy consumption dataset of households in italy and austria"

Connected through hyperedge HE-4 (induced by source paper "complexity of power draws for load disaggregation"):
  with members:
    - "autonomous load disaggregation approach based on active power measurements"
    - "greend an energy consumption dataset of households in italy and austria"
    - "load hiding of household s power demand"
    - "shannon information and kolmogorov complexity"

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)