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: "small loss bounds for online learning with partial information"
  Abstract: We consider the problem of adversarial (non-stochastic) online learning with partial information feedback, where at each stage, a decision maker picks an action from a finite set of possible actions. We develop a black-box approach to solving such problems where the learner observes as feedback only losses of a subset of the actions that include the selected action. Specifically, when losses of actions are non-negative, under the graph-based feedback model introduced by Mannor and Shamir, we offer algorithms that attain the so called "small-loss" regret bounds with high probability. Prior to our work, there was no data-dependent guarantee for general feedback graphs even for pseudo-regret (without dependence on the number of actions, i.e., taking advantage of the increased information feedback). Addressing this, we provide a high probability small-loss guarantee. Taking advantage of the black-box nature of our technique, we show applications to getting high probability small loss guarantees for semi-bandits (including routing in networks) and contextual bandits, including possibly infinite comparator class (such as infinite possible strategies in contextual bandits) as well as learning with slowly changing (shifting) comparators. #R##N#In the special case of classical bandit and semi-bandit problems, we provide optimal small-loss, high-probability guarantees of $\widetilde{O}(\sqrt{dL^{\star}})$ for the actual regret, where $d$ is the number of arms and $L^{\star}$ is the loss of the best arm or action, answering open questions of Neu. Previous work for bandits and semi-bandits offered analogous regret guarantee only for pseudo-regret and only in expectation.

Connected through hyperedge HE-1 (induced by source paper "bandits with adversarial scaling"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback"):
  with members:
    - "equipping experts bandits with long term memory"
    - "first order regret analysis of thompson sampling"
    - "bandits with feedback graphs and switching costs"
    - "improved path length regret bounds for bandits"
    - "online learning with feedback graphs and switching costs"
    - "make the minority great again first order regret bound for contextual bandits"
    - "online learning with graph structured feedback against adaptive adversaries"
    - "sparsity variance and curvature in multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "feedback graph regret bounds for thompson sampling and ucb"):
  with members:
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "reward maximization under uncertainty leveraging side observations on networks"
    - "online learning with feedback graphs without the graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"

Connected through hyperedge HE-4 (induced by source paper "stability and learning in strategic queuing systems"):
  with members:
    - "pacing equilibrium in first price auction markets"
    - "multiplicative pacing equilibria in auction markets"
    - "regret of queueing bandits"
    - "the fitness level method with tail bounds"
    - "composable and efficient mechanisms"

Connected through hyperedge HE-5 (induced by source paper "model selection for contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "estimating learnability in the sublinear data regime"
    - "learning to optimize under non stationarity"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"

Connected through hyperedge HE-6 (induced by source paper "first order bayesian regret analysis of thompson sampling"):
  with members:
    - "feedback graph regret bounds for thompson sampling and ucb"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "make the minority great again first order regret bound for contextual bandits"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "online learning with feedback graphs beyond bandits"
    - "an information theoretic analysis of thompson sampling"
    - "regret in online combinatorial optimization"

Connected through hyperedge HE-7 (induced by source paper "no regret learning from partially observed data in repeated auctions"):
  with members:
    - "no regret learning in unknown games with correlated payoffs"
    - "core selecting mechanisms in electricity markets"
    - "designing coalition proof reverse auctions over continuous goods"
    - "online learning with feedback graphs beyond bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "from bandits to experts on the value of side observations"

Connected through hyperedge HE-8 (induced by source paper "semi parametric dynamic contextual pricing"):
  with members:
    - "online learning with feedback graphs without the graphs"
    - "learning simple auctions"
    - "from bandits to experts a tale of domination and independence"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "leveraging side observations in stochastic bandits"
    - "from bandits to experts on the value of side observations"

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)