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: "towards minimax policies for online linear optimization with bandit feedback"
  Abstract: We address the online linear optimization problem with bandit feedback. Our contribution is twofold. First, we provide an algorithm (based on exponential weights) with a regret of order $\sqrt{d n \log N}$ for any finite action set with $N$ actions, under the assumption that the instantaneous loss is bounded by 1. This shaves off an extraneous $\sqrt{d}$ factor compared to previous works, and gives a regret bound of order $d \sqrt{n \log n}$ for any compact set of actions. Without further assumptions on the action set, this last bound is minimax optimal up to a logarithmic factor. Interestingly, our result also shows that the minimax regret for bandit linear optimization with expert advice in $d$ dimension is the same as for the basic $d$-armed bandit with expert advice. Our second contribution is to show how to use the Mirror Descent algorithm to obtain computationally efficient strategies with minimax optimal regret bounds in specific examples. More precisely we study two canonical action sets: the hypercube and the Euclidean ball. In the former case, we obtain the first computationally efficient algorithm with a $d \sqrt{n}$ regret, thus improving by a factor $\sqrt{d \log n}$ over the best known result for a computationally efficient algorithm. In the latter case, our approach gives the first algorithm with a $\sqrt{d n \log n}$ regret, again shaving off an extraneous $\sqrt{d}$ compared to previous works.

Connected through hyperedge HE-1 (induced by source paper "regret bounds for batched bandits"):
  with members:
    - "batch policy learning under constraints"
    - "adaptivity in adaptive submodularity"
    - "introduction to multi armed bandits"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "stochastic submodular cover with limited adaptivity"
    - "delay and cooperation in nonstochastic bandits"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "online learning with switching costs and other adaptive adversaries"

Connected through hyperedge HE-2 (induced by source paper "a near optimal algorithm for approximating the john ellipsoid"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "total least squares regression in input sparsity time"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "sensitivity sampling over dynamic geometric data streams with applications to k clustering"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"
    - "near optimal sketching of low rank tensor regression"
    - "sketching for kronecker product regression and p splines"

Connected through hyperedge HE-3 (induced by source paper "mirror descent view for neural network quantization"):
  with members:
    - "quantization networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxquant quantized neural networks via proximal operators"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "a survey on methods and theories of quantized neural networks"
    - "relaxed quantization for discretized neural networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"

Connected through hyperedge HE-4 (induced by source paper "exploration by optimisation in partial monitoring"):
  with members:
    - "on first order bounds variance and gap dependent bounds for adversarial bandits"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "connections between mirror descent thompson sampling and the information ratio"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "improved path length regret bounds for bandits"
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "more adaptive algorithms for adversarial bandits"

Connected through hyperedge HE-5 (induced by source paper "bandit convex optimization in non stationary environments"):
  with members:
    - "introduction to online convex optimization"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "corralling a band of bandit algorithms"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"

Connected through hyperedge HE-6 (induced by source paper "an information theoretic approach to minimax regret in partial monitoring"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs beyond bandits"
    - "towards optimal algorithms for prediction with expert advice"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "online linear optimization via smoothing"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-7 (induced by source paper "improved path length regret bounds for bandits"):
  with members:
    - "chasing convex bodies optimally"
    - "black box reductions for parameter free online learning in banach spaces"
    - "competitively chasing convex bodies"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"

Connected through hyperedge HE-8 (induced by source paper "online continuous submodular maximization from full information to bandit feedback"):
  with members:
    - "black box submodular maximization discrete and continuous settings"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "an optimal algorithm for bandit convex optimization"
    - "submodular functions from discrete to continous domains"
    - "online submodular maximization under a matroid constraint with application to learning assignments"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "projection free online learning"

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)