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: "differentially private mimo filtering for event streams and spatio temporal monitoring"
  Abstract: Many large-scale systems such as intelligent transportation systems, smart grids or smart buildings collect data about the activities of their users to optimize their operations. In a typical scenario, signals originate from many sensors capturing events involving these users, and several statistics of interest need to be continuously published in real-time. In addition, in order to encourage user participation, privacy issues need to be taken into consideration. This paper considers the problem of providing differential privacy guarantees for such multi-input multi-output systems operating continuously. We show in particular how to construct various extensions of the zero-forcing equalization mechanism, which we previously proposed for single-input single-output systems. We also describe an application to privately monitoring and forecasting occupancy in a building equipped with a dense network of motion detection sensors, which is useful for example to control its HVAC system.

Connected through hyperedge HE-1 (induced by source paper "error bounds and guidelines for privacy calibration in differentially private kalman filtering"):
  with members:
    - "differential privacy an economic method for choosing epsilon"
    - "differential privacy on the trade off between utility and information leakage"

Connected through hyperedge HE-2 (induced by source paper "preserving both privacy and utility in network trace anonymization"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "the risk utility tradeoff for ip address truncation"
    - "flaim a multi level anonymization framework for computer and network logs"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving nonlinear observer design using contraction analysis"):
  with members:
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "a differential lyapunov framework for contraction analysis"
    - "calibrating data to sensitivity in private data analysis"

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)