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: "differentially private distributed optimization"
  Abstract: In distributed optimization and iterative consensus literature, a standard problem is for $N$ agents to minimize a function $f$ over a subset of Euclidean space, where the cost function is expressed as a sum $\sum f_i$. In this paper, we study the private distributed optimization (PDOP) problem with the additional requirement that the cost function of the individual agents should remain differentially private. The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual's cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to the optimal value. Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm. We observe that to achieve $\epsilon$-differential privacy the accuracy of the algorithm has the order of $O(\frac{1}{\epsilon^2})$.

Connected through hyperedge HE-1 (induced by source paper "asynchronous federated learning with differential privacy for edge intelligence"):
  with members:
    - "differentially private learning with adaptive clipping"
    - "min entropy latent model for weakly supervised object detection"
    - "federated machine learning concept and applications"
    - "interpret federated learning with shapley values"
    - "exploiting unintended feature leakage in collaborative learning"
    - "mvg mechanism differential privacy under matrix valued query"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "recycled admm improving the privacy and accuracy of distributed algorithms"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "recycled admm improve privacy and accuracy with less computation in distributed algorithms"
    - "fast and differentially private algorithms for decentralized collaborative machine learning"
    - "adaptive consensus admm for distributed optimization"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "adaptive admm with spectral penalty parameter selection"

Connected through hyperedge HE-3 (induced by source paper "the value of collaboration in convex machine learning with differential privacy"):
  with members:
    - "chiron privacy preserving machine learning as a service"
    - "dp admm admm based distributed learning with differential privacy"
    - "privacy preserving machine learning through data obfuscation"
    - "learning differentially private recurrent language models"
    - "on the differential privacy of bayesian inference"
    - "functional mechanism regression analysis under differential privacy"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-4 (induced by source paper "privacy of agents costs in peer to peer distributed optimization"):
  with members:
    - "private learning on networks"
    - "privacy preserving linear programming"
    - "distributed optimization of convex sum of non convex functions"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-5 (induced by source paper "differentially private consensus based distributed optimization"):
  with members:
    - "efficient private erm for smooth objectives"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "privacy preservation in continuous time average consensus algorithm via deterministic additive perturbation signals"):
  with members:
    - "secure and privacy preserving average consensus"

Connected through hyperedge HE-7 (induced by source paper "resilient consensus for multi agent systems subject to differential privacy requirements"):
  with members:
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "resilient randomized quantized consensus"
    - "r robustness and r s robustness of circulant graphs"
    - "exploiting nodes symmetries to control synchronization and consensus patterns in multiagent systems"
    - "a jamming resilient algorithm for self triggered network coordination"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "differentially private iterative synchronous consensus"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "recycled admm improve privacy and accuracy with less computation in distributed algorithms"):
  with members:
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "distributed constraint optimization problems and applications a survey"
    - "dp admm admm based distributed learning with differential privacy"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "fast and differentially private algorithms for decentralized collaborative machine learning"
    - "adaptive consensus admm for distributed optimization"
    - "adaptive admm with spectral penalty parameter selection"
    - "distributed event localization via alternating direction method of multipliers"

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)