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: "a ptas for agnostically learning halfspaces"
  Abstract: We present a PTAS for agnostically learning halfspaces w.r.t. the uniform distribution on the $d$ dimensional sphere. Namely, we show that for every $\mu>0$ there is an algorithm that runs in time $\mathrm{poly}(d,\frac{1}{\epsilon})$, and is guaranteed to return a classifier with error at most $(1+\mu)\mathrm{opt}+\epsilon$, where $\mathrm{opt}$ is the error of the best halfspace classifier. This improves on Awasthi, Balcan and Long [ABL14] who showed an algorithm with an (unspecified) constant approximation ratio. Our algorithm combines the classical technique of polynomial regression (e.g. [LMN89, KKMS05]), together with the new localization technique of [ABL14].

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 "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-3 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "hardness of learning noisy halfspaces using polynomial thresholds"
    - "robust estimators in high dimensions without the computational intractability"
    - "pseudorandomness via the discrete fourier transform"
    - "complexity theoretic limitations on learning halfspaces"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "analysis of boolean functions"
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"

Connected through hyperedge HE-4 (induced by source paper "learning neural networks with two nonlinear layers in polynomial time"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "on the complexity of learning neural networks"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "electron proton dynamics in deep learning"
    - "sgd learns the conjugate kernel class of the network"
    - "convergence analysis of two layer neural networks with relu activation"

Connected through hyperedge HE-5 (induced by source paper "learning geometric concepts with nasty noise"):
  with members:
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "robust estimators in high dimensions without the computational intractability"
    - "complexity theoretic limitations on learning halfspaces"
    - "the power of localization for efficiently learning linear separators with noise"
    - "the average sensitivity of an intersection of half spaces"
    - "from average case complexity to improper learning complexity"

Connected through hyperedge HE-6 (induced by source paper "learning depth three neural networks in polynomial time"):
  with members:
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "convergence analysis of two layer neural networks with relu activation"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "complexity theoretic limitations on learning halfspaces"
    - "learning dnf expressions from fourier spectrum"

Connected through hyperedge HE-7 (induced by source paper "complexity theoretic limitations on learning halfspaces"):
  with members:
    - "how to refute a random csp"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "complexity theoretic limitations on learning dnf s"
    - "the power of localization for efficiently learning linear separators with noise"
    - "from average case complexity to improper learning complexity"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "on the complexity of random satisfiability problems with planted solutions"
    - "the complexity of learning halfspaces using generalized linear methods"

Connected through hyperedge HE-8 (induced by source paper "efficient learning of linear separators under bounded noise"):
  with members:
    - "the power of localization for efficiently learning linear separators with noise"
    - "from average case complexity to improper learning complexity"
    - "minimizing the misclassification error rate using a surrogate convex loss"

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.DS(Data Structures and Algorithms)