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: "bandits with switching costs t 2 3 regret"
  Abstract: We study the adversarial multi-armed bandit problem in a setting where the player incurs a unit cost each time he switches actions. We prove that the player's $T$-round minimax regret in this setting is $\widetilde{\Theta}(T^{2/3})$, thereby closing a fundamental gap in our understanding of learning with bandit feedback. In the corresponding full-information version of the problem, the minimax regret is known to grow at a much slower rate of $\Theta(\sqrt{T})$. The difference between these two rates provides the \emph{first} indication that learning with bandit feedback can be significantly harder than learning with full-information feedback (previous results only showed a different dependence on the number of actions, but not on $T$.) #R##N#In addition to characterizing the inherent difficulty of the multi-armed bandit problem with switching costs, our results also resolve several other open problems in online learning. One direct implication is that learning with bandit feedback against bounded-memory adaptive adversaries has a minimax regret of $\widetilde{\Theta}(T^{2/3})$. Another implication is that the minimax regret of online learning in adversarial Markov decision processes (MDPs) is $\widetilde{\Theta}(T^{2/3})$. The key to all of our results is a new randomized construction of a multi-scale random walk, which is of independent interest and likely to prove useful in additional settings.

Connected through hyperedge HE-1 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds"):
  with members:
    - "online learning over a finite action set with limited switching"
    - "k server via multiscale entropic regularization"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "the computational power of optimization in online learning"
    - "achieving all with no parameters adaptive normalhedge"
    - "an improved approximation for k median and positive correlation in budgeted optimization"

Connected through hyperedge HE-2 (induced by source paper "bandits with feedback graphs and switching costs"):
  with members:
    - "online learning with feedback graphs and switching costs"
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "online learning with abstention"
    - "corralling a band of bandit algorithms"
    - "online learning with feedback graphs without the graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"

Connected through hyperedge HE-3 (induced by source paper "learning adversarial mdps with bandit feedback and unknown transition"):
  with members:
    - "model free reinforcement learning in infinite horizon average reward markov decision processes"
    - "online convex optimization in adversarial markov decision processes"
    - "corruption robust exploration in episodic reinforcement learning"
    - "reinforcement learning under drift"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "regret minimization for reinforcement learning by evaluating the optimal bias function"
    - "drifting reinforcement learning the blessing of more optimism in face of endogenous exogenous dynamics"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "minimax regret of switching constrained online convex optimization no phase transition"):
  with members:
    - "online learning over a finite action set with limited switching"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "an optimal algorithm for bandit convex optimization"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "prediction by random walk perturbation"
    - "online learning with switching costs and other adaptive adversaries"
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-5 (induced by source paper "online learning with feedback graphs and switching costs"):
  with members:
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "bandits with movement costs and adaptive pricing"
    - "multi armed bandits with metric movement costs"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"
    - "on independent sets in random graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"

Connected through hyperedge HE-6 (induced by source paper "online learning with graph structured feedback against adaptive adversaries"):
  with members:
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "online learning with switching costs and other adaptive adversaries"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "from bandits to experts on the value of side observations"
    - "toward a classification of finite partial monitoring games"

Connected through hyperedge HE-7 (induced by source paper "truth and regret in online scheduling"):
  with members:
    - "stability of service under time of use pricing"
    - "era a framework for economic resource allocation for the cloud"
    - "truthful online scheduling with commitments"
    - "satzilla portfolio based algorithm selection for sat"
    - "optimal mechansim design and money burning"

Connected through hyperedge HE-8 (induced by source paper "a non stochastic learning approach to energy efficient mobility management"):
  with members:
    - "context aware mobility management in hetnets a reinforcement learning approach"
    - "an overview of load balancing in hetnets old myths and open problems"
    - "five disruptive technology directions for 5g"
    - "user association for load balancing in heterogeneous cellular networks"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)