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: "secure state estimation with byzantine sensors a probabilistic approach"
  Abstract: This paper studies static state estimation in multi-sensor settings, with a caveat that an unknown subset of the sensors are compromised by an adversary, whose measurements can be manipulated arbitrarily. A new performance metric, which quantifies the asymptotic decay rate for the probability of having an estimation error larger than $\delta$, is proposed. We develop an optimal estimator for the new performance metric with a fixed $\delta$, which is the Chebyshev center of a union of ellipsoids. We further provide an estimator that is optimal for every $\delta$, for the special case where the sensors are homogeneous. Numerical examples are given to elaborate the results.

Connected through hyperedge HE-1 (induced by source paper "byzantine fault tolerant distributed linear regression"):
  with members:
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "byzantine resilient distributed observers for lti systems"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"

Connected through hyperedge HE-2 (induced by source paper "secure distributed filtering for unstable dynamics under compromised observations"):
  with members:
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "detection and mitigation of biasing attacks on distributed estimation networks"
    - "convex optimization based state estimation against sparse integrity attacks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"

Connected through hyperedge HE-3 (induced by source paper "an optimization framework for resilient batch estimation in cyber physical systems"):
  with members:
    - "resilient state estimation for discrete time linear systems"
    - "on a class of optimization based robust estimators"
    - "convex optimization based state estimation against sparse integrity attacks"

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)