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: "privacy preserving linear programming"
  Abstract: With the rapid increase in computing, storage and networking resources, data is not only collected and stored, but also analyzed. This creates a serious privacy problem which often inhibits the use of this data. In this chapter, we investigate and resolve the privacy issues in a fundamental optimization problem -- linear programming (LP) which is formulated by data collected from different parties. We first consider the case where the objective function and constraints of the linear programming problem are not partitioned between two parties where one party privately holds the objective function while the other party privately holds the constraints. Second, we present a privacy preserving technique for the case that objective function and constraints are arbitrarily partitioned between two parties where each party privately holds a share of objective function and constraints. Finally, we extend the technique for securely solving two-party arbitrarily partitioned linear programming problems to a multi-party scenario. In summary, we propose a set of efficient and secure transformation based techniques that create significant value-added benefits of being independent of the specific algorithms used for solving the linear programming problem.

Connected through hyperedge HE-1 (induced by source paper "privacy of agents costs in peer to peer distributed optimization"):
  with members:
    - "private learning on networks"
    - "distributed optimization of convex sum of non convex functions"
    - "differentially private distributed optimization"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-2 (induced by source paper "securely solving the distributed graph coloring problem"):
  with members:
    - "privacy preserving and collusion resistant energy sharing"
    - "differentially private search log sanitization with optimal output utility"

Connected through hyperedge HE-3 (induced by source paper "private learning on networks part ii"):
  with members:
    - "deep learning with eigenvalue decay regularizer"
    - "towards geo distributed machine learning"
    - "private learning on networks"
    - "learning privately from multiparty data"
    - "distributed optimization of convex sum of non convex functions"
    - "federated optimization distributed optimization beyond the datacenter"
    - "differentially private distributed optimization"
    - "fast distributed gradient methods"

Connected through hyperedge HE-4 (induced by source paper "private learning on networks"):
  with members:
    - "towards geo distributed machine learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed optimization for client server architecture with negative gradient weights"
    - "distributed optimization of convex sum of non convex functions"
    - "differentially private distributed online learning"
    - "privacy preservation in distributed subgradient optimization algorithms"
    - "on the privacy of optimization approaches"

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.CR(Cryptography and Security)