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: "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs"
  Abstract: In linear stochastic bandits, it is commonly assumed that payoffs are with sub-Gaussian noises. In this paper, under a weaker assumption on noises, we study the problem of \underline{lin}ear stochastic {\underline b}andits with h{\underline e}avy-{\underline t}ailed payoffs (LinBET), where the distributions have finite moments of order $1+\epsilon$, for some $\epsilon\in (0,1]$. We rigorously analyze the regret lower bound of LinBET as $\Omega(T^{\frac{1}{1+\epsilon}})$, implying that finite moments of order 2 (i.e., finite variances) yield the bound of $\Omega(\sqrt{T})$, with $T$ being the total number of rounds to play bandits. The provided lower bound also indicates that the state-of-the-art algorithms for LinBET are far from optimal. By adopting median of means with a well-designed allocation of decisions and truncation based on historical information, we develop two novel bandit algorithms, where the regret upper bounds match the lower bound up to polylogarithmic factors. To the best of our knowledge, we are the first to solve LinBET optimally in the sense of the polynomial order on $T$. Our proposed algorithms are evaluated based on synthetic datasets, and outperform the state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "laplacian regularized graph bandits algorithms and theoretical analysis"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "alternating linear bandits for online matrix factorization recommendation"
    - "graph based recommendation system"
    - "horde of bandits using gaussian markov random fields"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "collaborative filtering bandits"
    - "learning laplacian matrix in smooth graph signal representations"
    - "online clustering of bandits"

Connected through hyperedge HE-2 (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"
    - "tsallis inf an optimal algorithm for stochastic and adversarial bandits"
    - "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-3 (induced by source paper "bayesian optimization under heavy tailed payoffs"):
  with members:
    - "high dimensional bayesian optimization via additive models with overlapping groups"
    - "on kernelized multi armed bandits"
    - "pac bayesian inequalities for martingales"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-4 (induced by source paper "thompson sampling on symmetric alpha stable bandits"):
  with members:
    - "learning to optimize via posterior sampling"
    - "further optimal regret bounds for thompson sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"

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)