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: "using more data to speed up training time"
  Abstract: In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we study the runtime of learning as a function of the number of available training examples, and underscore the main high-level techniques. We provide some initial positive results showing that the runtime can decrease exponentially while only requiring a polynomial growth of the number of examples, and spell-out several interesting open problems.

Connected through hyperedge HE-1 (induced by source paper "the computational power of optimization in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a counter example to karlin s strong conjecture for fictitious play"
    - "near optimal algorithms for online matrix prediction"
    - "efficient optimal learning for contextual bandits"
    - "sublinear optimization for machine learning"
    - "online learning via sequential complexities"

Connected through hyperedge HE-2 (induced by source paper "subsampled power iteration a unified algorithm for block models and planted csp s"):
  with members:
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "on the complexity of random satisfiability problems with planted solutions"
    - "community detection thresholds and the weak ramanujan property"
    - "graph spectra and the detectability of community structure in networks"
    - "hiding satisfying assignments two are better than one"

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)