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: "active and passive learning of linear separators under log concave distributions"
  Abstract: We provide new results concerning label efficient, polynomial time, passive and active learning of linear separators. We prove that active learning provides an exponential improvement over PAC (passive) learning of homogeneous linear separators under nearly log-concave distributions. Building on this, we provide a computationally efficient PAC algorithm with optimal (up to a constant factor) sample complexity for such problems. This resolves an open question concerning the sample complexity of efficient PAC algorithms under the uniform distribution in the unit ball. Moreover, it provides the first bound for a polynomial-time PAC algorithm that is tight for an interesting infinite class of hypothesis functions under a general and natural class of data-distributions, providing significant progress towards a longstanding open question. #R##N#We also provide new bounds for active and passive learning in the case that the data might not be linearly separable, both in the agnostic case and and under the Tsybakov low-noise condition. To derive our results, we provide new structural results for (nearly) log-concave distributions, which might be of independent interest as well.

Connected through hyperedge HE-1 (induced by source paper "adaptive region based active learning"):
  with members:
    - "efficient active learning of sparse halfspaces"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "diameter based active learning"
    - "efficient learning of linear separators under bounded noise"
    - "efficient and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "minimax analysis of active learning"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-2 (induced by source paper "noise tolerant reliable active classification with comparison queries"):
  with members:
    - "the power of comparisons for actively learning linear classifiers"
    - "sorting by swaps with noisy comparisons"
    - "generalized comparison trees for point location problems"
    - "optimal sorting with persistent comparison errors"
    - "active classification with comparison queries"
    - "efficient learning of linear separators under bounded noise"
    - "minimax analysis of active learning"
    - "sorting from noisy information"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for adversarially robust pac learning"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "empirically measuring concentration fundamental limits on intrinsic robustness"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "adversarial spheres"
    - "adversarial examples from cryptographic pseudo random generators"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"

Connected through hyperedge HE-4 (induced by source paper "active learning in the overparameterized and interpolating regime"):
  with members:
    - "two models of double descent for weak features"
    - "how do infinite width bounded norm networks look in function space"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "deep active learning for named entity recognition"
    - "cost effective active learning for deep image classification"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-5 (induced by source paper "the power of comparisons for actively learning linear classifiers"):
  with members:
    - "generalized comparison trees for point location problems"
    - "near optimal linear decision trees for k sum and related problems"
    - "active classification with comparison queries"
    - "conspiracies between learning algorithms circuit lower bounds and pseudorandomness"
    - "efficient learning of linear separators under bounded noise"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-6 (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"
    - "the power of localization for efficiently learning linear separators with noise"
    - "importance weighted active learning"

Connected through hyperedge HE-7 (induced by source paper "deep active learning with a neural architecture search"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "cost effective active learning for deep image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "exploring connections between active learning and model extraction"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "robust physical world attacks on deep learning models"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "practical black box attacks against machine learning"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "stealing machine learning models via prediction apis"

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)