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: "semi bandit optimization in the dispersed setting"
  Abstract: In this work, we study the problem of online optimization of piecewise Lipschitz functions with semi-bandit feedback. This challenging class of non-convex optimization problems often arises in algorithm selection problems for combinatorial settings, where the goal is to find the best algorithm from a large algorithm family for a specific application domain. In these settings, each evaluation of the loss functions in the optimization problem can be computationally expensive, often requiring the learner to run a combinatorial algorithm to measure its performance. Combined with the fact that small differences between similar algorithms in the family can lead to cascading changes in algorithm behavior, efficient online optimization in these settings is a challenging problem. However, we show that in many applications, evaluating the loss function for one algorithm choice can sometimes reveal the loss for a range of similar algorithms, essentially for free. We develop online optimization algorithms capable of using this kind of extra information by working in the semi-bandit feedback setting. Our algorithms achieve regret bounds that are essentially as good as algorithms under full-information feedback and are significantly more computationally efficient. #R##N#We apply our semi-bandit optimization results to obtain online algorithm selection procedures for two rich families of combinatorial algorithms. We provide the first provable guarantees for online algorithm selection for clustering problems using a family of clustering algorithms containing classic linkage procedures. We also show how to select algorithms from a family of greedy knapsack algorithms with simultaneously lower computational complexity and stronger regret bounds than the best algorithm selection procedures from prior work.

Connected through hyperedge HE-1 (induced by source paper "learning to optimize computational resources frugal training with generalization guarantees"):
  with members:
    - "learning to prune speeding up repeated computations"
    - "procrastinating with confidence near optimal anytime adaptive algorithm configuration"
    - "learning to link"
    - "learning to branch"
    - "learning robust search strategies using a bandit based approach"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "online optimization of smoothed piecewise constant functions"

Connected through hyperedge HE-2 (induced by source paper "online optimization of piecewise lipschitz functions in changing environments"):
  with members:
    - "introduction to online convex optimization"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "online optimization of smoothed piecewise constant functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "an algorithm for online k means clustering"

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)