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: "building energy load forecasting using deep neural networks"
  Abstract: Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past. However, it has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms. The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures were trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.

Connected through hyperedge HE-1 (induced by source paper "stacked boosters network architecture for short term load forecasting in buildings"):
  with members:
    - "a regressive convolution neural network and support vector regression model for electricity consumption forecasting"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep learning for time series forecasting the electric load case"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "comparative study of cnn and rnn for natural language processing"
    - "pixel recursive super resolution"
    - "convolutional sequence to sequence learning"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "generalizing semi supervised generative adversarial networks to regression using feature contrasting"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "convergence problems with generative adversarial networks gans"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "virtual to real reinforcement learning for autonomous driving"
    - "good semi supervised learning that requires a bad gan"
    - "improved training of wasserstein gans"
    - "semi and weakly supervised semantic segmentation using generative adversarial network"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-4 (induced by source paper "exploiting vulnerabilities of load forecasting through adversarial attacks"):
  with members:
    - "is machine learning in power systems vulnerable"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "tensorflow a system for large scale machine learning"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "powernet neural power demand forecasting in smart grid"):
  with members:
    - "lstm a search space odyssey"
    - "regression based online anomaly detection for smart grid data"
    - "energy consumption forecasting for smart meters"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "using an ancillary neural network to capture weekends and holidays in an adjoint neural network architecture for intelligent building management"):
  with members:
    - "lstm neural reordering feature for statistical machine translation"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "review of smart meter data analytics applications methodologies and challenges"):
  with members:
    - "the challenge of non technical loss detection using artificial intelligence a survey"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-8 (induced by source paper "recurrent transform learning"):
  with members:
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"

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)