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: "distribution system monitoring for smart power grids with distributed generation using artificial neural networks"
  Abstract: The increasing number of distributed generators connected to the distribution system at the low and medium voltage level requires a reliable monitoring of distribution grids. Economic considerations prevent a full observation of distribution grids with direct measurements. First state-of-the-art approaches using artificial neural networks (ANN) for monitoring distribution grids with a limited amount of measurements exist. These approaches, however, have strong limitations. We develop a new solution for distribution system monitoring overcoming these limitations by 1. presenting a novel training procedure for the ANN, enabling its use in distribution grids with a high amount of distributed generation and a very limited amount of measurements, far less than is traditionally required by the state-of-the-art Weighted Least Squares (WLS) state estimation (SE), 2. using mutliple hidden layers in the ANN, increasing the estimation accuracy, 3. including switch statuses as inputs to the ANN, eliminating the need for individual ANN for each switching state, 4. estimating line current magnitudes additionally to voltage magnitudes. Simulations performed with an elaborate evaluation approach on a real and a benchmark grid demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art ANN schemes and WLS SE under normal operating conditions and different situations such as gross measurement errors.

Connected through hyperedge HE-1 (induced by source paper "adversarial fdi attack against ac state estimation with ann"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "towards evaluating the robustness of neural networks"
    - "intriguing properties of 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.CE(Computational Engineering, Finance, and Science)