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: "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.

Connected through hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise"):
  with members:
    - "noise tolerant reliable active classification with comparison queries"
    - "a modern introduction to online learning"
    - "adaptive hard thresholding for near optimal consistent robust regression"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "efficient active learning of sparse halfspaces"
    - "active classification with comparison queries"
    - "a hitting time analysis of stochastic gradient langevin dynamics"

Connected through hyperedge HE-2 (induced by source paper "ltp a new active learning strategy for bert crf based named entity recognition"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep active learning for named entity recognition"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "learning halfspaces with massart noise under structured distributions"):
  with members:
    - "nonconvex sampling with the metropolis adjusted langevin algorithm"
    - "the complexity of finding stationary points with stochastic gradient descent"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "learning geometric concepts with nasty noise"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "efficient learning of linear separators under bounded noise"
    - "complexity theoretic limitations on learning halfspaces"

Connected through hyperedge HE-4 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

Connected through hyperedge HE-5 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-6 (induced by source paper "time accuracy tradeoffs for learning a relu with respect to gaussian marginals"):
  with members:
    - "complexity of training relu neural networks"
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "efficient active learning of sparse halfspaces"
    - "learning one convolutional layer with overlapping patches"
    - "the computational complexity of training relu s"
    - "learning two layer neural networks with symmetric inputs"
    - "learning one hidden layer neural networks with landscape design"
    - "learning geometric concepts with nasty noise"

Connected through hyperedge HE-7 (induced by source paper "distribution independent pac learning of halfspaces with massart noise"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"

Connected through hyperedge HE-8 (induced by source paper "disagreement based active learning in online settings"):
  with members:
    - "efficient active learning of sparse halfspaces"
    - "efficient learning of linear separators under bounded noise"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "importance weighted active learning"

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)