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: "how good is good enough re evaluating the bar for energy disaggregation"
  Abstract: Since the early 1980s, the research community has developed ever more sophisticated algorithms for the problem of energy disaggregation, but despite decades of research, there is still a dearth of applications with demonstrated value. In this work, we explore a question that is highly pertinent to this research community: how good does energy disaggregation need to be in order to infer characteristics of a household? We present novel techniques that use unsupervised energy disaggregation to predict both household occupancy and static properties of the household such as size of the home and number of occupants. Results show that basic disaggregation approaches performs up to 30% better at occupancy estimation than using aggregate power data alone, and are up to 10% better at estimating static household characteristics. These results show that even rudimentary energy disaggregation techniques are sufficient for improved inference of household characteristics. To conclude, we re-evaluate the bar set by the community for energy disaggregation accuracy and try to answer the question "how good is good enough?"

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"
    - "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "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 "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"):
  with members:
    - "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "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.LG(Machine Learning)