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 "learning optimal search algorithms from data"):
  Abstract: Classical algorithm design is geared towards worst case instances and fails to exploit structure that may be present in typical instances. Is it possible to learn this structure from examples and exploit it algorithmically? We study this question in the simplest algorithmic context -- search for a cheap solution within an unstructured space. This setting captures, for example, search for a short path to drive to work when only some routes may ever be relevant to consider, or shopping online when there may only be a handful of stores that offer the best prices. #R##N#We propose a framework for learning optimal search algorithms from data that captures the tradeoff between the cost of the solution and the time to find it. We consider a setting with $n$ alternatives each having an unknown cost that can be revealed one at a time. Given sample access to the distribution of the costs, our goal is to learn an algorithm that minimizes the expected sum of the cost of the chosen alternative and the total time to find it. #R##N#Algorithms for this problem fall into three different classes, non-adaptive which always query a fixed set of alternatives, partially-adaptive that query alternatives in a fixed order until they decide to stop and fully-adaptive that choose the next query based on the costs they've seen. While approximately optimal fully-adaptive strategies cannot be learned efficiently, our main result is that it is possible to learn a partially-adaptive strategy that approximates the best non-adaptive and partially-adaptive strategies efficiently both in terms of samples and computation. #R##N#We extend our results to settings where multiple alternatives must be chosen and study the case where any $k$ alternatives are feasible and the case where the alternatives must form a matroid base e.g. picking a minimum cost spanning tree.

Includes members:
  Member "learning to prune speeding up repeated computations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to optimize computational resources frugal training with generalization guarantees")
  Member "the markovian price of information":
    also connected through other hyperedges:
      (none)
  Member "learning to branch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")
  Member "competitive caching with machine learned advice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online convex optimization for caching networks")
  Member "leapsandbounds a method for approximately optimal algorithm configuration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "the price of information in combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pandora s box problem with order constraints")
  Member "dispersion for data driven algorithm design online learning and private optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "adaptivity gaps for stochastic probing submodular and xos functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pandora s box problem with order constraints")

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.DS(Data Structures and Algorithms)