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: "greend an energy consumption dataset of households in italy and austria"
  Abstract: Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "assisted energy management in smart microgrids"):
  with members:
    - "integration of legacy appliances into home energy management systems"
    - "a game theoretic approach to energy trading in the smart grid"

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"
    - "an empirical investigation of v i trajectory based load signatures for non intrusive load monitoring"

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"
    - "load hiding of household s power demand"
    - "an empirical investigation of v i trajectory based load signatures for non intrusive load monitoring"
    - "shannon information and kolmogorov complexity"

Connected through hyperedge HE-5 (induced by source paper "an open solution to provide personalized feedback for building energy management"):
  with members:
    - "yomo the arduino based smart metering board"
    - "metadata for energy disaggregation"

Connected through hyperedge HE-6 (induced by source paper "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"):
  with members:
    - "metadata for energy disaggregation"
    - "a comparison of non intrusive load monitoring methods for commercial and residential buildings"

Connected through hyperedge HE-7 (induced by source paper "autonomous load disaggregation approach based on active power measurements"):
  with members:
    - "load hiding of household s power demand"

Connected through hyperedge HE-8 (induced by source paper "proficiency of power values for load disaggregation"):
  with members:
    - "complexity of power draws for load disaggregation"

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.OH(Other Computer Science)