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: "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
  Abstract: This paper considers the problem of minimizing the expected value of a (possibly nonconvex) cost function parameterized by a random (vector) variable, when the expectation cannot be computed accurately (e.g., because the statistics of the random variables are unknown and/or the computational complexity is prohibitive). Classical stochastic gradient methods for solving this problem may suffer from slow convergence. In this paper, we propose a stochastic parallel Successive Convex Approximation-based (best-response) algorithm for general nonconvex stochastic sum-utility optimization problems, which arise naturally in the design of multi-agent networks. The proposed novel decomposition approach enables all users to update their optimization variables in parallel by solving a sequence of strongly convex subproblems, one for each user. Almost sure convergence to stationary points is proved. We then customize the algorithmic framework to solve the stochastic sum rate maximization problem over single-input-single-output (SISO) frequency-selective interference channels, multiple-input-multiple-output (MIMO) interference channels, and MIMO multiple-access channels. Numerical results corroborate that the proposed algorithms can converge faster than state-of-the-art stochastic gradient schemes while achieving the same (or better) sum-rates.

Connected through hyperedge HE-1 (induced by source paper "convergent policy optimization for safe reinforcement learning"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "provably efficient q learning with low switching cost"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "stochastic successive convex approximation for non convex constrained stochastic optimization"
    - "learning safe policies with expert guidance"
    - "communication efficient distributed reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "robust federated learning with noisy communication"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "scheduling policies for federated learning in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-3 (induced by source paper "power control for massive mimo systems with nonorthogonal pilots"):
  with members:
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "pilot contamination mitigation with reduced rf chains"
    - "massive mimo has unlimited capacity"
    - "joint pilot design and uplink power allocation in multi cell massive mimo systems"
    - "optimal pilot and payload power control in single cell massive mimo systems"
    - "distributed massive mimo in cellular networks impact of imperfect hardware and number of oscillators"

Connected through hyperedge HE-4 (induced by source paper "stochastic successive convex approximation for non convex constrained stochastic optimization"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "online stochastic optimization with multiple objectives"
    - "probabilistic sinr constrained robust transmit beamforming a bernstein type inequality based conservative approach"

Connected through hyperedge HE-5 (induced by source paper "two timescale hybrid compression and forward for massive mimo aided c ran"):
  with members:
    - "optimized uplink transmission in multi antenna c ran with spatial compression and forward"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-6 (induced by source paper "stochastic successive convex approximation for general stochastic optimization problems"):
  with members:
    - "stochastic successive convex approximation for non convex constrained stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"):
  with members:
    - "joint power allocation and user association optimization for massive mimo systems"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "hybrid beamforming for massive mimo a survey"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "hierarchical interference mitigation for massive mimo cellular networks"
    - "low complexity hybrid precoding in massive multiuser mimo systems"

Connected through hyperedge HE-8 (induced by source paper "unraveling the rank one solution mystery of robust miso downlink transmit optimization a verifiable sufficient condition via a new duality result"):
  with members:
    - "coordinate update algorithms for robust power loading for the mu miso downlink with outage constraints"
    - "a stochastic beamformed amplify and forward scheme in a multigroup multicast mimo relay network with per antenna power constraints"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"
    - "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"
    - "worst case robust multiuser transmit beamforming using semidefinite relaxation duality and implications"
    - "robust linear precoder design for multi cell downlink transmission"
    - "outage constrained robust transmit optimization for multiuser miso downlinks tractable approximations by conic 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.MA(Multiagent Systems)