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: "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
  Abstract: We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x = x*. The objective function of the corresponding optimization problem is the sum of private (known only by a node), convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, de-centralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping), and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping). The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures), the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.

Connected through hyperedge HE-1 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "on convergence of emphatic temporal difference learning"

Connected through hyperedge HE-2 (induced by source paper "linear speedup in saddle point escape for decentralized non convex optimization"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "escaping saddles with stochastic gradients"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "communication efficient distributed dual coordinate ascent"

Connected through hyperedge HE-3 (induced by source paper "fully decentralized multi agent reinforcement learning with networked agents"):
  with members:
    - "diff dac distributed actor critic for multitask deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "network flows that solve linear equations"):
  with members:
    - "decentralized gradient algorithm for solution of a linear equation"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "solving systems of linear equations locally and asynchronously"
    - "distributed strongly convex optimization"
    - "fast distributed gradient methods"
    - "randomized optimal consensus of multi agent systems"
    - "reaching an optimal consensus dynamical systems that compute intersections of convex sets"

Connected through hyperedge HE-5 (induced by source paper "distributed resource allocation for epidemic control"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "decentralized protection strategies against sis epidemics in networks"
    - "the cost of an epidemic over a complex network a random matrix approach"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"

Connected through hyperedge HE-6 (induced by source paper "distributed continuous time approximate projection protocols for shortest distance optimization problems"):
  with members:
    - "multi agent robust consensus convergence analysis and application"
    - "randomized optimal consensus of multi agent systems"
    - "reaching an optimal consensus dynamical systems that compute intersections of convex sets"
    - "zero gradient sum algorithms for distributed convex optimization the continuous time case"

Connected through hyperedge HE-7 (induced by source paper "multitask diffusion adaptation over asynchronous networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "distributed diffusion based lms for node specific parameter estimation over adaptive networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part ii performance analysis"

Connected through hyperedge HE-8 (induced by source paper "distributed compressed sensing for static and time varying networks"):
  with members:
    - "distributed soft thresholding for sparse signal recovery"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "a fast distributed proximal gradient method"
    - "distributed sparse signal recovery for sensor networks"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "iterative hard thresholding for compressed sensing"

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.IT(Information Theory)