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: "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
  Abstract: We examine 12 studies on the efficacy of disaggregated energy feedback. The average electricity reduction across these studies is 4.5%. However, 4.5% may be a positively-biased estimate of the savings achievable across the entire population because all 12 studies are likely to be prone to opt-in bias hence none test the effect of disaggregated feedback on the general population. Disaggregation may not be required to achieve these savings: Aggregate feedback alone drives 3% reductions; and the 4 studies which directly compared aggregate feedback against disaggregated feedback found that aggregate feedback is at least as effective as disaggregated feedback, possibly because web apps are viewed less often than in-home-displays (in the short-term, at least) and because some users do not trust fine-grained disaggregation (although this may be an issue with the specific user interface studied). Disaggregated electricity feedback may help a motivated sub-group of the population to save more energy but fine-grained disaggregation may not be necessary to achieve these energy savings. Disaggregation has many uses beyond those discussed in this paper but, on the specific question of promoting energy reduction in the general population, there is no robust evidence that current forms of disaggregated energy feedback are more effective than aggregate energy feedback. The effectiveness of disaggregated feedback may increase if the general population become more energy-conscious (e.g. if energy prices rise or concern about climate change deepens); or if users' trust in fine-grained disaggregation improves; or if innovative new approaches or alternative disaggregation strategies (e.g. disaggregating by behaviour rather than by appliance) out-perform existing feedback. We also discuss opportunities for new research into the effectiveness of disaggregated feedback.

Connected through hyperedge HE-1 (induced by source paper "active collaborative sensing for energy breakdown"):
  with members:
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "how good is good enough re evaluating the bar for energy disaggregation"
    - "nilmtk v0 2 a non intrusive load monitoring toolkit for large scale data sets demo abstract"
    - "a contextual bandit approach to personalized news article recommendation"
    - "active learning with statistical models"

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"
    - "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization"
    - "sparse modeling for image and vision processing"
    - "an empirical investigation of v i trajectory based load signatures for non intrusive load monitoring"

Connected through hyperedge HE-3 (induced by source paper "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"):
  with members:
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "how good is good enough re evaluating the bar for energy disaggregation"
    - "greend an energy consumption dataset of households in italy and austria"
    - "nilmtk v0 2 a non intrusive load monitoring toolkit for large scale data sets demo abstract"

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)