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: "byzantine resilient distributed observers for lti systems"
  Abstract: Consider a linear time-invariant (LTI) dynamical system monitored by a network of sensors, modeled as nodes of an underlying directed communication graph. We study the problem of collaboratively estimating the state of the system when certain nodes are compromised by adversaries. Specifically, we consider a Byzantine adversary model, where a compromised node possesses complete knowledge of the system dynamics and the network, and can deviate arbitrarily from the rules of any prescribed algorithm. We first characterize certain fundamental limitations of any distributed state estimation algorithm in terms of the measurement and communication structure of the nodes. We then develop an attack-resilient, provably correct state estimation algorithm that admits a fully distributed implementation. To characterize feasible network topologies that guarantee success of our proposed technique, we introduce a notion of `strong-robustness' that captures both measurement and communication redundancy. Finally, by drawing connections to bootstrap percolation theory, we argue that given an LTI system and an associated sensor network, the `strong-robustness' property can be checked in polynomial time.

Connected through hyperedge HE-1 (induced by source paper "byzantine fault tolerant distributed linear regression"):
  with members:
    - "secure state estimation with byzantine sensors a probabilistic approach"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "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 "a new approach for distributed hypothesis testing with extensions to byzantine resilience"):
  with members:
    - "distributed observers for lti systems"
    - "defending non bayesian learning against adversarial attacks"
    - "distributed optimization under adversarial nodes"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-3 (induced by source paper "a filtering approach for resiliency of distributed observers against smart spoofers"):
  with members:
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "on the limited communication analysis and design for decentralized estimation"
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "resilient randomized quantized consensus"

Connected through hyperedge HE-4 (induced by source paper "detection and mitigation of biasing attacks on distributed estimation networks"):
  with members:
    - "controller jammer game models of denial of service in control systems operating over packet dropping links"
    - "detection of biasing attacks on distributed estimation networks"
    - "distributed observers for lti systems"
    - "gain scheduled synchronization of parameter varying systems via relative h consensus with application to synchronization of uncertain bilinear systems"
    - "detectability of distributed consensus based observer networks an elementary analysis and extensions"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "conditions for detectability in distributed consensus based observer networks"

Connected through hyperedge HE-5 (induced by source paper "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"):
  with members:
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine vector consensus in complete graphs"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-6 (induced by source paper "finite time distributed state estimation over time varying graphs exploiting the age of information"):
  with members:
    - "a distributed observer for a time invariant linear system"
    - "a hybrid observer for a distributed linear system with a changing neighbor graph"
    - "distributed observers for lti systems"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "conditions for detectability in distributed consensus based observer 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.SY(Systems and Control)