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: "the challenge of non technical loss detection using artificial intelligence a survey"
  Abstract: Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.

Connected through hyperedge HE-1 (induced by source paper "electricity theft detection with self attention"):
  with members:
    - "stand alone self attention in vision models"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi scale densenet based electricity theft detection"
    - "multi scale context aggregation by dilated convolutions"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "missing data prediction and classification the use of auto associative neural networks and optimization algorithms"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "efficient power theft detection for residential consumers using mean shift data mining knowledge discovery process"):
  with members:
    - "identifying irregular power usage by turning predictions into holographic spatial visualizations"
    - "deep convolutional neural networks for anomaly event classification on distributed systems"
    - "a review of mean shift algorithms for clustering"

Connected through hyperedge HE-3 (induced by source paper "review of smart meter data analytics applications methodologies and challenges"):
  with members:
    - "building energy load forecasting using deep neural networks"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-4 (induced by source paper "identifying irregular power usage by turning predictions into holographic spatial visualizations"):
  with members:
    - "is big data sufficient for a reliable detection of non technical losses"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "xgboost a scalable tree boosting system"
    - "large scale detection of non technical losses in imbalanced data sets"

Connected through hyperedge HE-5 (induced by source paper "is big data sufficient for a reliable detection of non technical losses"):
  with members:
    - "large scale detection of non technical losses in imbalanced data sets"

Connected through hyperedge HE-6 (induced by source paper "neighborhood features help detecting electricity theft in big data sets"):
  with members:
    - "large scale detection of non technical losses in imbalanced data sets"

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.AI(Artificial Intelligence)