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 stochastic optimization via matrix exponential learning"
  Abstract: In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communications. The proposed algorithm relies on the method of matrix exponential learning (MXL) and only requires locally computable gradient observations that are possibly imperfect. To analyze it, we introduce the notion of a stable Nash equilibrium and we show that the algorithm is globally convergent to such equilibria—or locally convergent when an equilibrium is only locally stable. To complement our convergence analysis, we also derive explicit bounds for the algorithm's convergence speed and we test it in realistic multicarrier/multiple-antenna wireless scenarios where several users seek to maximize their energy efficiency. Our results show that learning allows users to attain a net increase between $100\%$ and $500\%$ in energy efficiency, even under very high uncertainty.

Connected through hyperedge HE-1 (induced by source paper "matrix exponential learning schemes with low informational exchange"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "optimal design of energy efficient multi user mimo systems is massive mimo the answer"

Connected through hyperedge HE-2 (induced by source paper "learning in time varying games"):
  with members:
    - "bandit learning in concave n person games"
    - "cycles in adversarial regularized learning"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "online optimization competing with dynamic comparators"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-3 (induced by source paper "bandit learning in concave n person games"):
  with members:
    - "learning with minimal information in continuous games"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "fast convergence of regularized learning in games"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "online convex optimization and no regret learning algorithms guarantees and applications"):
  with members:
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"
    - "a survey on metric learning for feature vectors and structured data"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "distributed stochastic optimization in networks with low informational exchange"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"

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)