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: "large scale detection of non technical losses in imbalanced data sets"
  Abstract: Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.

Connected through hyperedge HE-1 (induced by source paper "non technical loss detection with statistical profile images based on semi supervised learning"):
  with members:
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "temporal ensembling for semi supervised learning"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "imaging time series to improve classification and imputation"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "identifying irregular power usage by turning predictions into holographic spatial visualizations"):
  with members:
    - "the challenge of non technical loss detection using artificial intelligence a survey"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "the challenge of non technical loss detection using artificial intelligence a survey"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "is big data sufficient for a reliable detection of non technical losses"):
  with members:
    - "the challenge of non technical loss detection using artificial intelligence a survey"

Connected through hyperedge HE-5 (induced by source paper "neighborhood features help detecting electricity theft in big data sets"):
  with members:
    - "the challenge of non technical loss detection using artificial intelligence a survey"

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)