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 constrained optimization by consensus based primal dual perturbation method"
  Abstract: Various distributed optimization methods have been developed for solving problems which have simple local constraint sets and whose objective function is the sum of local cost functions of distributed agents in a network. Motivated by emerging applications in smart grid and distributed sparse regression, this paper studies distributed optimization methods for solving general problems which have a coupled global cost function and have inequality constraints. We consider a network scenario where each agent has no global knowledge and can access only its local mapping and constraint functions. To solve this problem in a distributed manner, we propose a consensus-based distributed primal-dual perturbation (PDP) algorithm. In the algorithm, agents employ the average consensus technique to estimate the global cost and constraint functions via exchanging messages with neighbors, and meanwhile use a local primal-dual perturbed subgradient method to approach a global optimum. The proposed PDP method not only can handle smooth inequality constraints but also non-smooth constraints such as some sparsity promoting constraints arising in sparse optimization. We prove that the proposed PDP algorithm converges to an optimal primal-dual solution of the original problem, under standard problem and network assumptions. Numerical results illustrating the performance of the proposed algorithm for a distributed demand response control problem in smart grid are also presented.

Connected through hyperedge HE-1 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures"):
  with members:
    - "spherical text embedding"
    - "straggler mitigation with tiered gradient codes"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "a reinforcement learning based approach for joint multi agent decision making"
    - "vstore a data store for analytics on large videos"
    - "optimal server selection for straggler mitigation"
    - "formal security analysis of neural networks using symbolic intervals"

Connected through hyperedge HE-2 (induced by source paper "supervised learning under distributed features"):
  with members:
    - "coordinate descent diffusion learning by networked agents"
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "variance reduced stochastic learning under random reshuffling"
    - "distributed learning for stochastic generalized nash equilibrium problems"
    - "sdca without duality regularization and individual convexity"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"
    - "dictionary learning over distributed models"

Connected through hyperedge HE-3 (induced by source paper "gadmm fast and communication efficient framework for distributed machine learning"):
  with members:
    - "wireless network intelligence at the edge"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "proximity without consensus in online multiagent optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed mean estimation with limited communication"
    - "distributed optimization with arbitrary local solvers"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-4 (induced by source paper "network utility maximization based on incentive mechanism for truthful reporting of local information"):
  with members:
    - "applications of economic and pricing models for wireless network security a survey"
    - "joint scheduling and transmission power control in wireless ad hoc networks"
    - "distributed and fair beaconing rate adaptation for congestion control in vehicular networks"
    - "energy aware traffic offloading for green heterogeneous networks"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "energy efficiency and spectral efficiency tradeoff in device to device d2d communications"

Connected through hyperedge HE-5 (induced by source paper "dadam a consensus based distributed adaptive gradient method for online optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "d 2 decentralized training over decentralized data"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "efficient regret minimization in non convex games"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "energy efficient massive mimo through distributed precoder design"):
  with members:
    - "joint user scheduling and beam selection optimization for beam based massive mimo downlinks"
    - "harvest the potential of massive mimo with multi layer techniques"
    - "green 5g heterogeneous networks through dynamic small cell operation"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "massive mimo ten myths and one critical question"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "massive mimo meets hetnet interference coordination through spatial blanking"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"

Connected through hyperedge HE-7 (induced by source paper "networked parallel algorithms for robust convex optimization via the scenario approach"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "robust regression and lasso"

Connected through hyperedge HE-8 (induced by source paper "a duality based approach for distributed optimization with coupling constraints"):
  with members:
    - "a duality based approach for distributed min max optimization with application to demand side management"
    - "a proximal dual consensus admm method for multi agent constrained optimization"
    - "a polyhedral approximation framework for convex and robust 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)