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 "on covariate shift adaptation via sparse filtering"):
  Abstract: A major challenge in machine learning is covariate shift, i.e., the problem of training data and test data coming from different distributions. This paper studies the feasibility of tackling this problem by means of sparse filtering. We show that the sparse filtering algorithm intrinsically addresses this problem, but it has limited capacity for covariate shift adaptation. To overcome this limit, we propose a novel semi-supervised sparse filtering algorithm, named periodic sparse filtering. Our proposed algorithm is formally analyzed and empirically evaluated with an elaborated synthetic data set and real speech emotion data sets. As a result, we argue that, as an alternative methodology, feature distribution learning has enormous potential in carrying out covariate shift adaptation.

Includes members:
  Member "towards understanding sparse filtering a theoretical perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards further understanding of sparse filtering via information bottleneck")
  Member "mathematics of sparsity and entropy axioms core functions and sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards understanding sparse filtering a theoretical perspective")

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)