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: "neural nilm deep neural networks applied to energy disaggregation"
  Abstract: Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.

Connected through hyperedge HE-1 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-2 (induced by source paper "deep learning based energy disaggregation and on off detection of household appliances"):
  with members:
    - "subtask gated networks for non intrusive load monitoring"
    - "a wavenet for speech denoising"
    - "language modeling with gated convolutional networks"
    - "sdp relaxation with randomized rounding for energy disaggregation"
    - "wavenet a generative model for raw audio"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "non intrusive load monitoring with an attention based deep neural network"):
  with members:
    - "recent trends in deep learning based natural language processing"
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"
    - "image captioning with semantic attention"
    - "listen attend and spell"
    - "feed forward networks with attention can solve some long term memory problems"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (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"
    - "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"
    - "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-5 (induced by source paper "neural network for nilm based on operational state change classification"):
  with members:
    - "on the feasibility of generic deep disaggregation for single load extraction"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "transfer learning for non intrusive load monitoring"):
  with members:
    - "non intrusive load monitoring with fully convolutional networks"
    - "subtask gated networks for non intrusive load monitoring"
    - "on the feasibility of generic deep disaggregation for single load extraction"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "training free non intrusive load monitoring of electric vehicle charging with low sampling rate"

Connected through hyperedge HE-7 (induced by source paper "on metrics to assess the transferability of machine learning models in non intrusive load monitoring"):
  with members:
    - "transfer learning for non intrusive load monitoring"
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"

Connected through hyperedge HE-8 (induced by source paper "subtask gated networks for non intrusive load monitoring"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an overview of multi task learning in deep neural networks"
    - "sdp relaxation with randomized rounding for energy disaggregation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "sequence to sequence learning with neural networks"

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.NE(Neural and Evolutionary Computing)