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 "rapid learning with stochastic focus of attention"):
  Abstract: We present a method to determine when to stop the evaluation of a decision-making process. The method determines to stop the evaluation process when the result of the full evaluation is obvious. This trait is highly desirable for margin-based Machine Learning algorithms where a classifier traditionally evaluates all the features for every example. However, some examples are easier to classify than others, a phenomenon which is characterized by the event when most of the features agree on the class of an example. By stopping the feature evaluation when encountering an easy to classify example, a margin-based Machine Learning algorithm can achieve substantial reduction in running times. #R##N#To determine when to stop the feature evaluation, we develop a set of novel sequential tests, the Sequential Thresholded Sum Tests (STST). These tests stop the partial evaluation of the sum when the result of the full summation is guaranteed with high probability. By making different assumptions on the data and the features different tests arise. In general we look at the feature evaluation process as a random walk and apply different Brownian motion early stopping inequalities to determine when to stop the walk. From these inequalities we derive a family of stopping thresholds for sequential feature evaluations under different assumptions. #R##N#We demonstrate the effectiveness of the different STST by speeding up several Online Learning algorithms on synthetic and real data.

Includes members:
  Member "efficient learning with partially observed attributes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "classification with costly features as a sequential decision making problem")
  Member "sublinear optimization for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on coresets for support vector machines")

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)