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: "multisensor sequential change detection with unknown change propagation pattern"
  Abstract: Detecting changes with multiple sensors has received significant attention in the literature. In many practical applications such as critical infrastructure monitoring and modeling of disease spread, a useful change propagation model is one where change eventually happens at all sensors, but where not all sensors witness change at the same time-instant. While the known change propagation dynamics was studied before, this paper studies a more general setting of unknown change propagation pattern (trajectory) in both centralized and decentralized settings with the goal of detecting the first time-instant any sensor witnesses a change. Using the dynamic programming framework, the optimal solution structures are derived and in the special rare-change regime, several more practical solutions are proposed. Under certain conditions, the first-order asymptotic optimality of a proposed algorithm called multichart test is shown as the false alarm probability vanishes. Moreover, low-complexity solutions are proposed based on online estimation of the change propagation pattern. Numerical studies illustrate that the proposed detectors offer near-optimal performance. Furthermore, in the decentralized setting, the level-crossing sampling is proposed for sampling and transmission of local statistics that significantly improves the detection performance using the same amount of communication resources compared to the conventional uniform-in-time sampling.

Connected through hyperedge HE-1 (induced by source paper "real time nonparametric anomaly detection in high dimensional settings"):
  with members:
    - "secure distributed dynamic state estimation in wide area smart grids"
    - "real time detection of hybrid and stealthy cyber attacks in smart grid"
    - "online cyber attack detection in smart grid a reinforcement learning approach"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "anomaly detection with score functions based on nearest neighbor graphs"
    - "a kernel method for the two sample problem"

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.IT(Information Theory)