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: "private learning on networks"
  Abstract: Continual data collection and widespread deployment of machine learning algorithms, particularly the distributed variants, have raised new privacy challenges. In a distributed machine learning scenario, the dataset is stored among several machines and they solve a distributed optimization problem to collectively learn the underlying model. We present a secure multi-party computation inspired privacy preserving distributed algorithm for optimizing a convex function consisting of several possibly non-convex functions. Each individual objective function is privately stored with an agent while the agents communicate model parameters with neighbor machines connected in a network. We show that our algorithm can correctly optimize the overall objective function and learn the underlying model accurately. We further prove that under a vertex connectivity condition on the topology, our algorithm preserves privacy of individual objective functions. We establish limits on the what a coalition of adversaries can learn by observing the messages and states shared over a network.

Connected through hyperedge HE-1 (induced by source paper "privacy of agents costs in peer to peer distributed optimization"):
  with members:
    - "privacy preserving linear programming"
    - "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 "statistical privacy in distributed average consensus on bounded real inputs"):
  with members:
    - "secure and privacy preserving average consensus"
    - "differentially private iterative synchronous consensus"

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"
    - "learning privately from multiparty data"
    - "privacy preserving linear programming"
    - "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 "distributed optimization for client server architecture with negative gradient weights"):
  with members:
    - "distributed optimization of convex sum of non convex functions"
    - "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)