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: "adaptive communication networks with privacy guarantees"
  Abstract: Utilizing the concept of observability, in conjunction with tools from graph theory and optimization, this paper develops an algorithm for network synthesis with privacy guarantees. In particular, we propose an algorithm for the selection of optimal weights for the communication graph in order to maximize the privacy of nodes in the network, from a control theoretic perspective. In this direction, we propose an observability-based design of the communication topology that improves the privacy of the network in presence of an intruder. The resulting adaptive network responds to the intrusion by changing the topology of the network-in an online manner- in order to reduce the information exposed to the intruder.

Connected through hyperedge HE-1 (induced by source paper "a system theoretic framework for privacy preservation in continuous time multiagent dynamics"):
  with members:
    - "dynamical privacy in distributed computing part i privacy loss and ppsc mechanism"
    - "a dynamical approach to privacy preserving average consensus"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "secure and privacy preserving average consensus"
    - "differentially private iterative synchronous consensus"

Connected through hyperedge HE-2 (induced by source paper "dynamics based privacy protection for average consensus on directed graphs"):
  with members:
    - "secure and privacy preserving average consensus"
    - "private and accurate data aggregation against dishonest nodes"
    - "differentially private distributed optimization"
    - "differentially private iterative synchronous consensus"

Connected through hyperedge HE-3 (induced by source paper "a dynamical approach to privacy preserving average consensus"):
  with members:
    - "dynamical privacy in distributed computing part i privacy loss and ppsc mechanism"
    - "secure and privacy preserving average consensus"
    - "differentially private iterative synchronous consensus"

Connected through hyperedge HE-4 (induced by source paper "secure and privacy preserving average consensus"):
  with members:
    - "differentially private distributed optimization"

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)