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: "distributed optimization of convex sum of non convex functions"
  Abstract: We present a distributed solution to optimizing a convex function composed of several non-convex functions. Each non-convex function is privately stored with an agent while the agents communicate with neighbors to form a network. We show that coupled consensus and projected gradient descent algorithm proposed in [1] can optimize convex sum of non-convex functions under an additional assumption on gradient Lipschitzness. We further discuss the applications of this analysis in improving privacy in distributed optimization.

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"
    - "privacy preserving linear programming"
    - "differentially private distributed optimization"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-2 (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"
    - "privacy preserving linear programming"
    - "federated optimization distributed optimization beyond the datacenter"
    - "differentially private distributed optimization"
    - "fast distributed gradient methods"

Connected through hyperedge HE-3 (induced by source paper "private learning on networks"):
  with members:
    - "towards geo distributed machine learning"
    - "privacy preserving linear programming"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed optimization for client server architecture with negative gradient weights"
    - "differentially private distributed online learning"
    - "privacy preservation in distributed subgradient optimization algorithms"
    - "on the privacy of optimization approaches"

Connected through hyperedge HE-4 (induced by source paper "distributed optimization for client server architecture with negative gradient weights"):
  with members:
    - "private learning on networks"
    - "tensorflow a system for large scale machine learning"
    - "fault tolerant multi agent optimization part iii"

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.DC(Distributed, Parallel, and Cluster Computing)