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 active learning of sparse halfspaces"
  Abstract: We study the problem of efficient PAC active learning of homogeneous linear classifiers (halfspaces) in $\mathbb{R}^d$, where the goal is to learn a halfspace with low error using as few label queries as possible. Under the extra assumption that there is a $t$-sparse halfspace that performs well on the data ($t \ll d$), we would like our active learning algorithm to be {\em attribute efficient}, i.e. to have label requirements sublinear in $d$. In this paper, we provide a computationally efficient algorithm that achieves this goal. Under certain distributional assumptions on the data, our algorithm achieves a label complexity of $O(t \cdot \mathrm{polylog}(d, \frac 1 \epsilon))$. In contrast, existing algorithms in this setting are either computationally inefficient, or subject to label requirements polynomial in $d$ or $\frac 1 \epsilon$.

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"
    - "active classification with comparison queries"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"

Connected through hyperedge HE-2 (induced by source paper "adaptive region based active learning"):
  with members:
    - "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"
    - "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 "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"
    - "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"
    - "learning relus via gradient descent"

Connected through hyperedge HE-4 (induced by source paper "disagreement based active learning in online settings"):
  with members:
    - "efficient learning of linear separators under bounded noise"
    - "minimax analysis of active learning"
    - "the power of localization for efficiently learning linear separators with noise"
    - "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)