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: "robust distributed routing in dynamical flow networks"
  Abstract: Robustness of distributed routing policies is studied for dynamical flow networks, with respect to adversarial disturbances that reduce the link flow capacities. A dynamical flow network is modeled as a system of ordinary differential equations derived from mass conservation laws on a directed acyclic graph with a single origin-destination pair and a constant inflow at the origin. Distributed routing policies regulate the way the incoming flow at a non-destination node gets split among its outgoing links as a function of the local information about the current particle density, while the outflow of a link is modeled to depend on the current particle density through a flow function. A dynamical flow network is called fully transferring if the outflow at the destination node is asymptotically equal to the inflow at the origin node, and partially transferring if the outflow at the destination node is asymptotically bounded away from zero. A class of distributed routing policies that are locally responsive is shown to yield the maximum possible resilience under local information constraint with respect to the two transferring properties, where resilience is measured as the minimum, among all the disturbances that make the network loose its transferring property, of the sum of the link-wise magnitude of disturbances. Finally, a simple convex optimization problem is formulated for the most resilient initial equilibrium flow, and the use of tolls to induce such an initial equilibrium flow in transportation networks is discussed.

Connected through hyperedge HE-1 (induced by source paper "characterization and control of diffusion processes in multi agent networks"):
  with members:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "distributed pareto optimization via diffusion strategies"
    - "opinion fluctuations and disagreement in social networks"
    - "gossip algorithms for distributed signal processing"

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)