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: "how much data is sufficient to learn high performing algorithms"
  Abstract: Algorithms -- for example for scientific analysis -- typically have tunable parameters that significantly influence computational efficiency and solution quality. If a parameter setting leads to strong algorithmic performance on average over a set of training instances, that parameter setting -- ideally -- will perform well on previously unseen future instances. However, if the set of training instances is too small, average performance will not generalize to future performance. This raises the question: how large should this training set be? We answer this question for any algorithm satisfying an easy-to-describe, ubiquitous property: its performance is a piecewise-structured function of its parameters. We provide the first unified sample complexity framework for algorithm parameter configuration; prior research followed case-by-case analyses. We present example applications to diverse domains including biology, political science, economics, integer programming, and clustering.

Connected through hyperedge HE-1 (induced by source paper "learning complexity of simulated annealing"):
  with members:
    - "learning to link"
    - "learning to branch"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"

Connected through hyperedge HE-2 (induced by source paper "learning to link"):
  with members:
    - "data driven clustering via parameterized lloyd s families"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "local algorithms for interactive clustering"
    - "clustering under perturbation resilience"
    - "center based clustering under perturbation stability"

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)