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: "tsallis inf an optimal algorithm for stochastic and adversarial bandits"
  Abstract: We derive an algorithm that achieves the optimal (within constants) pseudo-regret in both adversarial and stochastic multi-armed bandits without prior knowledge of the regime and time horizon. 1 The algorithm is based on online mirror descent with Tsallis entropy regularizer. We provide a complete characterization of such algorithms and show that Tsallis entropy with power $\alpha=1/2$ achieves the goal. In addition, the proposed algorithm enjoys improved regret guarantees in two intermediate regimes: stochastic bandits with adversarial corruptions introduced by Lykouris et al., and the stochastically constrained adversary studied by Wei and Luo. The algorithm also achieves adversarial and stochastic optimality in the utility-based dueling bandit setting. We provide empirical evaluation of the algorithm demonstrating that it outperforms UCB1 and EXP3 in stochastic environments. We also provide examples of adversarial environments, where UCB1 and Thompson Sampling exhibit almost linear regret, whereas our algorithm suffers only "logarithmic" regret.

Connected through hyperedge HE-1 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "semi parametric dynamic contextual pricing"
    - "manipulating a learning defender and ways to counteract"
    - "contextual search via intrinsic volumes"
    - "stochastic bandits robust to adversarial corruptions"

Connected through hyperedge HE-2 (induced by source paper "private stochastic convex optimization efficient algorithms for non smooth objectives"):
  with members:
    - "introduction to online convex optimization"
    - "concentrated differential privacy"
    - "fighting bandits with a new kind of smoothness"
    - "renyi divergence and kullback leibler divergence"
    - "calibrating data to sensitivity in private data analysis"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"

Connected through hyperedge HE-4 (induced by source paper "better algorithms for stochastic bandits with adversarial corruptions"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "stochastic bandits robust to adversarial corruptions"
    - "efficient algorithms for outlier robust regression"
    - "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs"
    - "learning geometric concepts with nasty noise"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "learning from untrusted data"
    - "agnostic estimation of mean and covariance"

Connected through hyperedge HE-5 (induced by source paper "stochastic linear optimization with adversarial corruption"):
  with members:
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "the best of both worlds stochastic and adversarial bandits"
    - "contextual bandit algorithms with supervised learning guarantees"

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)