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 "tighter bound estimation of sensitivity analysis for incremental and decremental data modification"):
  Abstract: In large-scale classification problems, the data set may be faced with frequent updates, e.g., a small ratio of data is added to or removed from the original data set. In this case, incremental learning, which updates an existing classifier by explicitly modeling the data modification, is more efficient than retraining a new classifier from scratch. Conventional incremental learning algorithms try to solve the problem exactly. However, for some tasks, we are only interested in the lower and upper bound for some values relevant to the coefficient vector of the updated classifier without really solving it, e.g., determining whether we should update the classifier or performing some sensitivity analysis tasks. To deal with these such tasks, we propose an algorithm to make rational inferences about the updated classifier with low computational complexity. Specifically, we present a method to calculate tighter bounds of a general linear score for the updated classifier such that it's more accurate to estimate the range of interest than existing papers. The proposed method can be applied to any linear classifiers with differentiable convex L2 regularization loss function. Both theoretical analysis and experiment results show that the proposed approach is superior to existing methods.

Includes members:
  Member "screening tests for lasso problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe squeezing for antisparse coding")
  Member "a safe screening rule for sparse logistic regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "safe screening with variational inequalities and its application to lasso":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "safe feature elimination for the lasso and sparse supervised learning problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe screening for the generalized conditional gradient method")

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)