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: "co simulation state of the art"
  Abstract: It is essential to find new ways of enabling experts in different disciplines to collaborate more efficient in the development of ever more complex systems, under increasing market pressures. One possible solution for this challenge is to use a heterogeneous model-based approach where different teams can produce their conventional models and carry out their usual mono-disciplinary analysis, but in addition, the different models can be coupled for simulation (co-simulation), allowing the study of the global behavior of the system. Due to its potential, co-simulation is being studied in many different disciplines but with limited sharing of findings. Our aim with this work is to summarize, bridge, and enhance future research in this multidisciplinary area. #R##N#We provide an overview of co-simulation approaches, research challenges, and research opportunities, together with a detailed taxonomy with different aspects of the state of the art of co-simulation and classification for the past five years. The main research needs identified are: finding generic approaches for modular, stable and accurate coupling of simulation units; and expressing the adaptations required to ensure that the coupling is correct.

Connected through hyperedge HE-1 (induced by source paper "analyzing cyber physical systems from the perspective of artificial intelligence"):
  with members:
    - "adversarial resilience learning towards systemic vulnerability analysis for large and complex systems"
    - "gray box adversarial testing for control systems with machine learning component"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "practical black box attacks against machine learning"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "an empirical survey on co simulation promising standards challenges and research needs"):
  with members:
    - "co simulation of continuous systems a tutorial"
    - "energy conservation and power bonds in co simulations non iterative adaptive step size control and error estimation"
    - "hybrid simulation safety limbos and zero crossings"

Connected through hyperedge HE-3 (induced by source paper "topological semantics for lumped parameter systems modeling"):
  with members:
    - "chain based representations for solid and physical modeling"

Connected through hyperedge HE-4 (induced by source paper "energy conservation and coupling error reduction in non iterative co simulations"):
  with members:
    - "energy conservation and power bonds in co simulations non iterative adaptive step size control and error estimation"

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.SY(Systems and Control)