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: "efficient learning of linear separators under bounded noise"
  Abstract: We study the learnability of linear separators in $\Re^d$ in the presence of bounded (a.k.a Massart) noise. This is a realistic generalization of the random classification noise model, where the adversary can flip each example $x$ with probability $\eta(x) \leq \eta$. We provide the first polynomial time algorithm that can learn linear separators to arbitrarily small excess error in this noise model under the uniform distribution over the unit ball in $\Re^d$, for some constant value of $\eta$. While widely studied in the statistical learning theory community in the context of getting faster convergence rates, computationally efficient algorithms in this model had remained elusive. Our work provides the first evidence that one can indeed design algorithms achieving arbitrarily small excess error in polynomial time under this realistic noise model and thus opens up a new and exciting line of research. #R##N#We additionally provide lower bounds showing that popular algorithms such as hinge loss minimization and averaging cannot lead to arbitrarily small excess error under Massart noise, even under the uniform distribution. Our work instead, makes use of a margin based technique developed in the context of active learning. As a result, our algorithm is also an active learning algorithm with label complexity that is only a logarithmic the desired excess error $\epsilon$.

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 "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 and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

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"
    - "complexity theoretic limitations on learning halfspaces"
    - "analysis of boolean functions"

Connected through hyperedge HE-4 (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"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "sorting from noisy information"

Connected through hyperedge HE-5 (induced by source paper "diameter based interactive structure discovery"):
  with members:
    - "diameter based active learning"
    - "a general framework for robust interactive learning"
    - "interactive bayesian hierarchical clustering"
    - "equality of opportunity in supervised learning"
    - "clustering with same cluster queries"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "local algorithms for interactive clustering"
    - "robust interactive learning"

Connected through hyperedge HE-6 (induced by source paper "grinding the space learning to classify against strategic agents"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "strategizing against no regret learners"
    - "introduction to multi armed bandits"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "competing prediction algorithms"
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"

Connected through hyperedge HE-7 (induced by source paper "noise tolerant fair classification"):
  with members:
    - "agnostic federated learning"
    - "fairness risk measures"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "differentially private fair learning"
    - "fairness through computationally bounded awareness"

Connected through hyperedge HE-8 (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"

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)