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 robust proper learning of log concave distributions"
  Abstract: We study the {\em robust proper learning} of univariate log-concave distributions (over continuous and discrete domains). Given a set of samples drawn from an unknown target distribution, we want to compute a log-concave hypothesis distribution that is as close as possible to the target, in total variation distance. In this work, we give the first computationally efficient algorithm for this learning problem. Our algorithm achieves the information-theoretically optimal sample size (up to a constant factor), runs in polynomial time, and is robust to model misspecification with nearly-optimal error guarantees. #R##N#Specifically, we give an algorithm that, on input $n=O(1/\eps^{5/2})$ samples from an unknown distribution $f$, runs in time $\widetilde{O}(n^{8/5})$, and outputs a log-concave hypothesis $h$ that (with high probability) satisfies $\dtv(h, f) = O(\opt)+\eps$, where $\opt$ is the minimum total variation distance between $f$ and the class of log-concave distributions. Our approach to the robust proper learning problem is quite flexible and may be applicable to many other univariate distribution families.

Connected through hyperedge HE-1 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples"):
  with members:
    - "towards testing monotonicity of distributions over general posets"
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "on learning mixtures of well separated gaussians"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing bayesian networks"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"
    - "properly learning poisson binomial distributions in almost polynomial time"

Connected through hyperedge HE-2 (induced by source paper "a polynomial time algorithm for log concave maximum likelihood via locally exponential families"):
  with members:
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "an efficient algorithm for high dimensional log concave maximum likelihood"
    - "density estimation for shift invariant multidimensional distributions"
    - "a polynomial time algorithm for maximum likelihood estimation of multivariate log concave densities"
    - "a size free clt for poisson multinomials and its applications"
    - "learning multivariate log concave distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "properly learning poisson binomial distributions in almost polynomial time"

Connected through hyperedge HE-3 (induced by source paper "on the sample complexity of learning sum product networks"):
  with members:
    - "bayesian learning of sum product networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "sample efficient learning of mixtures"
    - "expectation maximization for sum product networks as exponential family mixture models"
    - "a unified approach for learning the parameters of sum product networks"
    - "on the expressive efficiency of sum product networks"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

Connected through hyperedge HE-4 (induced by source paper "poison frogs targeted clean label poisoning attacks on neural networks"):
  with members:
    - "when does machine learning fail generalized transferability for evasion and poisoning attacks"
    - "certified defenses for data poisoning attacks"
    - "generative poisoning attack method against neural networks"
    - "learning under p tampering attacks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "testing shape restrictions of discrete distributions"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "optimal testing for properties of distributions"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"
    - "optimal algorithms and lower bounds for testing closeness of structured distributions"
    - "testing poisson binomial distributions"
    - "testing identity of structured distributions"

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)