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: "kernel based methods for bandit convex optimization"
  Abstract: We consider the adversarial convex bandit problem and we build the first $\mathrm{poly}(T)$-time algorithm with $\mathrm{poly}(n) \sqrt{T}$-regret for this problem. To do so we introduce three new ideas in the derivative-free optimization literature: (i) kernel methods, (ii) a generalization of Bernoulli convolutions, and (iii) a new annealing schedule for exponential weights (with increasing learning rate). The basic version of our algorithm achieves $\tilde{O}(n^{9.5} \sqrt{T})$-regret, and we show that a simple variant of this algorithm can be run in $\mathrm{poly}(n \log(T))$-time per step at the cost of an additional $\mathrm{poly}(n) T^{o(1)}$ factor in the regret. These results improve upon the $\tilde{O}(n^{11} \sqrt{T})$-regret and $\exp(\mathrm{poly}(T))$-time result of the first two authors, and the $\log(T)^{\mathrm{poly}(n)} \sqrt{T}$-regret and $\log(T)^{\mathrm{poly}(n)}$-time result of Hazan and Li. Furthermore we conjecture that another variant of the algorithm could achieve $\tilde{O}(n^{1.5} \sqrt{T})$-regret, and moreover that this regret is unimprovable (the current best lower bound being $\Omega(n \sqrt{T})$ and it is achieved with linear functions). For the simpler situation of zeroth order stochastic convex optimization this corresponds to the conjecture that the optimal query complexity is of order $n^3 / \epsilon^2$.

Connected through hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "learning to optimize under non stationarity"
    - "iterate averaging as regularization for stochastic gradient descent"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "make the minority great again first order regret bound for contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "competing against equilibria in zero sum games with evolving payoffs"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "the mechanics of n player differentiable games"
    - "on convergence and stability of gans"
    - "faster rates for convex concave games"
    - "adversarial bandits with knapsacks"
    - "began boundary equilibrium generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "improved regret bounds for projection free bandit convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "an optimal algorithm for bandit convex optimization"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "distributed online optimization with long term constraints"):
  with members:
    - "introduction to online convex optimization"
    - "distributed online linear regression"
    - "online convex optimization for cumulative constraints"
    - "trading regret for efficiency online convex optimization with long term constraints"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "a stochastic view of optimal regret through minimax duality"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-5 (induced by source paper "wireless optimisation via convex bandits unlicensed lte wifi coexistence"):
  with members:
    - "introduction to online convex optimization"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "fair coexistence of scheduled and random access wireless networks unlicensed lte wifi"
    - "multi armed bandits with application to 5g small cells"
    - "an optimal algorithm for bandit convex optimization"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "combinatorial bandits revisited"
    - "unlicensed lte wifi coexistence is lbt inherently fairer than csat"

Connected through hyperedge HE-6 (induced by source paper "more adaptive algorithms for adversarial bandits"):
  with members:
    - "introduction to online convex optimization"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "small loss bounds for online learning with partial information"
    - "sparsity variance and curvature in multi armed bandits"
    - "corralling a band of bandit algorithms"
    - "metagrad multiple learning rates in online learning"
    - "coin betting and parameter free online learning"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-7 (induced by source paper "low rank bandit methods for high dimensional dynamic pricing"):
  with members:
    - "perishability of data dynamic pricing under varying coefficient models"
    - "online learning with low rank experts"
    - "contextual bandits with latent confounders an nmf approach"
    - "low rank bandits with latent mixtures"
    - "on graduated optimization for stochastic non convex problems"
    - "the best of both worlds stochastic and adversarial bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "online newton step algorithm with estimated gradient"):
  with members:
    - "online learning a comprehensive survey"
    - "stochastic optimization with bandit sampling"
    - "online learning to rank in stochastic click models"
    - "an optimal algorithm for bandit convex optimization"
    - "on graduated optimization for stochastic non convex problems"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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.LG(Machine Learning)