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: "beating stochastic and adversarial semi bandits optimally and simultaneously"
  Abstract: We develop the first general semi-bandit algorithm that simultaneously achieves $\mathcal{O}(\log T)$ regret for stochastic environments and $\mathcal{O}(\sqrt{T})$ regret for adversarial environments without knowledge of the regime or the number of rounds $T$. The leading problem-dependent constants of our bounds are not only optimal in some worst-case sense studied previously, but also optimal for two concrete instances of semi-bandit problems. Our algorithm and analysis extend the recent work of (Zimmert & Seldin, 2019) for the special case of multi-armed bandit, but importantly requires a novel hybrid regularizer designed specifically for semi-bandit. Experimental results on synthetic data show that our algorithm indeed performs well uniformly over different environments. We finally provide a preliminary extension of our results to the full bandit feedback.

Connected through hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "online second price auction with semi bandit feedback under the non stationary setting"
    - "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "learning to optimize under non stationarity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "bandits with adversarial scaling"):
  with members:
    - "improved path length regret bounds for bandits"
    - "competing bandits the perils of exploration under competition"
    - "factored bandits"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "bandit regret scaling with the effective loss range"
    - "competing bandits learning under competition"

Connected through hyperedge HE-3 (induced by source paper "corruption robust exploration in episodic reinforcement learning"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "recent advances in algorithmic high dimensional robust statistics"
    - "worst case regret bounds for exploration via randomized value functions"

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"
    - "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"
    - "sparsity variance and curvature in multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"):
  with members:
    - "combinatorial semi bandit in the non stationary environment"
    - "introduction to online convex optimization"
    - "bandit online learning with unknown delays"
    - "thompson sampling for combinatorial semi bandits"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "sparsity variance and curvature in multi armed bandits"
    - "collaborative filtering bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-6 (induced by source paper "better algorithms for stochastic bandits with adversarial corruptions"):
  with members:
    - "stochastic bandits robust to adversarial corruptions"
    - "efficient algorithms for outlier robust regression"
    - "tsallis inf an optimal algorithm for stochastic and adversarial bandits"
    - "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-7 (induced by source paper "equipping experts bandits with long term memory"):
  with members:
    - "improved path length regret bounds for bandits"
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "sparsity variance and curvature in multi armed bandits"
    - "strongly adaptive online learning"
    - "the best of both worlds stochastic and adversarial bandits"

Connected through hyperedge HE-8 (induced by source paper "an optimal algorithm for adversarial bandits with arbitrary delays"):
  with members:
    - "nonstochastic multiarmed bandits with unrestricted delays"
    - "efficient online portfolio with logarithmic regret"
    - "sparsity variance and curvature in multi armed bandits"
    - "delay and cooperation in nonstochastic bandits"
    - "online learning under delayed feedback"

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)