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: "evaluating power system vulnerability to false data injection attacks via scalable optimization"
  Abstract: Physical consequences to power systems of false data injection cyber-attacks are considered. Prior work has shown that the worst-case consequences of such an attack can be determined using a bi-level optimization problem, wherein an attack is chosen to maximize the physical power flow on a target line subsequent to re-dispatch. This problem can be solved as a mixed-integer linear program, but it is difficult to scale to large systems due to numerical challenges. Three new computationally efficient algorithms to solve this problem are presented. These algorithms provide lower and upper bounds on the system vulnerability measured as the maximum power flow subsequent to an attack. Using these techniques, vulnerability assessments are conducted for IEEE 118-bus system and Polish system with 2383 buses.

Connected through hyperedge HE-1 (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"
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"

Connected through hyperedge HE-2 (induced by source paper "enhancing power system cyber security with systematic two stage detection strategy"):
  with members:
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"

Connected through hyperedge HE-3 (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"
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"

Connected through hyperedge HE-4 (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"
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-5 (induced by source paper "vulnerability assessment of large scale power systems to false data injection attacks"):
  with members:
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"

Connected through hyperedge HE-6 (induced by source paper "can attackers with limited information exploit historical data to mount successful false data injection attacks on power systems"):
  with members:
    - "vulnerability analysis and consequences of false data injection attack on power system state estimation"

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)