human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "data driven clustering via parameterized lloyd s families":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "leapsandbounds a method for approximately optimal algorithm configuration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "dispersion for data driven algorithm design online learning and private optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "a general theory of sample complexity for multi item profit maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limitations of incentive compatibility on discrete type spaces")
  Member "online optimization of smoothed piecewise constant functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "k center clustering under perturbation resilience":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension")
  Member "a pac approach to application specific algorithm selection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning complexity of simulated annealing")

Question: Please tell me which class the source paper 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)