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 "the power of localization for efficiently learning linear separators with noise"):
  Abstract: We introduce a new approach for designing computationally efficient learning algorithms that are tolerant to noise. We demonstrate the effectiveness of our approach by designing algorithms with improved noise tolerance guarantees for learning linear separators. #R##N#We consider the malicious noise model of Valiant and the adversarial label noise model of Kearns, Schapire, and Sellie. For malicious noise, where the adversary can corrupt an $\eta$ of fraction both the label part and the feature part, we provide a polynomial-time algorithm for learning linear separators in $\Re^d$ under the uniform distribution with near information-theoretic optimal noise tolerance of $\eta = \Omega(\epsilon)$. We also get similar improvements for the adversarial label noise model. We obtain similar results for more general classes of distributions including isotropic log-concave distributions. #R##N#In addition, our algorithms achieve a label complexity whose dependence on the error parameter $\epsilon$ is {\em exponentially better} than that of any passive algorithm. This provides the first polynomial-time active learning algorithm for learning linear separators in the presence of adversarial label noise, as well as the first analysis of active learning under the malicious noise model.

Includes members:
  Member "robust estimators in high dimensions without the computational intractability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "efficient learning of linear separators under bounded noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "learning with a drifting target concept":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model adaptation and unsupervised learning with non stationary batch data under smooth concept drift")
  Member "a ptas for agnostically learning halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "beyond disagreement based agnostic active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive region based active learning")
  Member "active and passive learning of linear separators under log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive region based active learning")
  Member "robust interactive learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diameter based interactive structure discovery")
  Member "agnostic active learning without constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")

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)