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: "learning geometric concepts with nasty noise"
  Abstract: We study the efficient learnability of geometric concept classes - specifically, low-degree polynomial threshold functions (PTFs) and intersections of halfspaces - when a fraction of the data is adversarially corrupted. We give the first polynomial-time PAC learning algorithms for these concept classes with dimension-independent error guarantees in the presence of nasty noise under the Gaussian distribution. In the nasty noise model, an omniscient adversary can arbitrarily corrupt a small fraction of both the unlabeled data points and their labels. This model generalizes well-studied noise models, including the malicious noise model and the agnostic (adversarial label noise) model. Prior to our work, the only concept class for which efficient malicious learning algorithms were known was the class of origin-centered halfspaces. #R##N#Specifically, our robust learning algorithm for low-degree PTFs succeeds under a number of tame distributions -- including the Gaussian distribution and, more generally, any log-concave distribution with (approximately) known low-degree moments. For LTFs under the Gaussian distribution, we give a polynomial-time algorithm that achieves error $O(\epsilon)$, where $\epsilon$ is the noise rate. At the core of our PAC learning results is an efficient algorithm to approximate the low-degree Chow-parameters of any bounded function in the presence of nasty noise. To achieve this, we employ an iterative spectral method for outlier detection and removal, inspired by recent work in robust unsupervised learning. Our aforementioned algorithm succeeds for a range of distributions satisfying mild concentration bounds and moment assumptions. The correctness of our robust learning algorithm for intersections of halfspaces makes essential use of a novel robust inverse independence lemma that may be of broader interest.

Connected through hyperedge HE-1 (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"
    - "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"
    - "analysis of boolean functions"

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 "recent advances in algorithmic high dimensional robust statistics"):
  with members:
    - "faster algorithms for high dimensional robust covariance estimation"
    - "list decodable linear regression"
    - "high dimensional robust estimation of sparse models via trimmed hard thresholding"
    - "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"
    - "how hard is robust mean estimation"
    - "list decodable learning via sum of squares"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "high dimensional estimation via sum of squares proofs"

Connected through hyperedge HE-4 (induced by source paper "faster algorithms for high dimensional robust covariance estimation"):
  with members:
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"

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"
    - "being robust in high dimensions can be practical"
    - "hardness of learning noisy halfspaces using polynomial thresholds"

Connected through hyperedge HE-6 (induced by source paper "robust algorithms for the secretary problem"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "stochastic bandits robust to adversarial corruptions"
    - "learning from untrusted data"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online and random order load balancing simultaneously"

Connected through hyperedge HE-7 (induced by source paper "outlier robust high dimensional sparse estimation via iterative filtering"):
  with members:
    - "high dimensional robust estimation of sparse models via trimmed hard thresholding"
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "certified defenses for data poisoning attacks"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-8 (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 relus via gradient descent"

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)