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: "vulnerability analysis and consequences of false data injection attack on power system state estimation"
  Abstract: An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization problem is introduced whose objective is to maximize the physical line flows subsequent to an FDI attack on DC SE. The maximization is subject to constraints on both attacker resources (size of attack) and attack detection (limiting load shifts) as well as those required by DC optimal power flow (OPF) following SE. The resulting attacks are tested on a more realistic non-linear system model using AC state estimation and ACOPF, and it is shown that, with an appropriately chosen sub-network, the attacker can overload transmission lines with moderate shifts of load.

Connected through hyperedge HE-1 (induced by source paper "state of the art of cyber physical systems security an automatic control perspective"):
  with members:
    - "on redundant observability from security index to attack detection and resilient state estimation"
    - "detection of sensor attack and resilient state estimation for uniformly observable nonlinear systems having redundant sensors"
    - "analysis of stochastic switched systems with application to networked control under jamming attacks"
    - "coding schemes for securing cyber physical systems against stealthy data injection attacks"
    - "detecting integrity attacks on control systems using a moving target approach"
    - "a moving horizon hybrid stochastic game for secure control of cyber physical systems"
    - "modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements"
    - "state estimation with secrecy against eavesdroppers"

Connected through hyperedge HE-2 (induced by source paper "can predictive filters detect gradually ramping false data injection attacks against pmus"):
  with members:
    - "vulnerability assessment of n 1 reliable power systems to false data injection attacks"
    - "unobservable false data injection attacks against pmus feasible conditions and multiplicative attacks"
    - "false data injection attacks on phasor measurements that bypass low rank decomposition"
    - "evaluating power system vulnerability to false data injection attacks via scalable optimization"

Connected through hyperedge HE-3 (induced by source paper "a survey on state estimation techniques and challenges in smart distribution systems"):
  with members:
    - "review of smart meter data analytics applications methodologies and challenges"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "robust recovery of missing data in electricity distribution systems"
    - "topology detection in microgrids with micro synchrophasors"

Connected through hyperedge HE-4 (induced by source paper "vulnerability analysis of smart grids to gps spoofing"):
  with members:
    - "identification of successive unobservable cyber data attacks in power systems through matrix decomposition"
    - "secure state estimation for nonlinear power systems under cyber attacks"
    - "risk mitigation for dynamic state estimation against cyber attacks and unknown inputs"

Connected through hyperedge HE-5 (induced by source paper "enhancing power system cyber security with systematic two stage detection strategy"):
  with members:
    - "evaluating power system vulnerability to false data injection attacks via scalable optimization"

Connected through hyperedge HE-6 (induced by source paper "unobservable false data injection attacks against pmus feasible conditions and multiplicative attacks"):
  with members:
    - "can attackers with limited information exploit historical data to mount successful false data injection attacks on power systems"
    - "identification of successive unobservable cyber data attacks in power systems through matrix decomposition"
    - "evaluating power system vulnerability to false data injection attacks via scalable optimization"

Connected through hyperedge HE-7 (induced by source paper "application of correlation indices on intrusion detection systems protecting the power grid against coordinated attacks"):
  with members:
    - "developing a correlation index to identify coordinated cyber attacks to power grids"

Connected through hyperedge HE-8 (induced by source paper "false data injection attacks on phasor measurements that bypass low rank decomposition"):
  with members:
    - "can attackers with limited information exploit historical data to mount successful false data injection attacks on power systems"
    - "identification of successive unobservable cyber data attacks in power systems through matrix decomposition"
    - "evaluating power system vulnerability to false data injection attacks via scalable optimization"
    - "robust pca via outlier pursuit"

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)