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: "modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one class classification"
  Abstract: Detecting faults in electrical power grids is of paramount importance, both from the electricity operator and consumer point of view. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all components belonging to the whole infrastructure (e.g., cables and related insulation, transformers, and breakers). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid are collected, such as meteorological information. Designing an efficient recognition model to discriminate faults in real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of dissimilarity measures learning and one-class classification techniques. We provide here an in-depth study related to the available data and to the models based on the proposed one-class classification approach. Furthermore, we perform a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based decision rule.

Connected through hyperedge HE-1 (induced by source paper "an overview and comparative analysis of recurrent neural networks for short term load forecasting"):
  with members:
    - "lstm a search space odyssey"
    - "temporal overdrive recurrent neural network"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "identifying user habits through data mining on call data records"
    - "wavenet a generative model for raw audio"
    - "architectural complexity measures of recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "one class classifiers based on entropic spanning graphs"):
  with members:
    - "toward a multilevel representation of protein molecules"
    - "fast and simple computation of top k closeness centralities"
    - "entropic one class classifiers"
    - "a review of feature selection methods based on mutual information"

Connected through hyperedge HE-3 (induced by source paper "a novel mechanical analogy based battery model for soc estimation using a multicell ekf"):
  with members:
    - "on the impact of topological properties of smart grids in power losses optimization problems"

Connected through hyperedge HE-4 (induced by source paper "entropic one class classifiers"):
  with members:
    - "designing labeled graph classifiers by exploiting the renyi entropy of the dissimilarity representation"
    - "building pattern recognition applications with the spare library"

Connected through hyperedge HE-5 (induced by source paper "position paper a general framework for applying machine learning techniques in operating room"):
  with members:
    - "an agent based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery"

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)