human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a size free clt for poisson multinomials and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private hypothesis selection")
  Member "learning multivariate log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a polynomial time algorithm for log concave maximum likelihood via locally exponential families")
  Member "a nearly optimal and agnostic algorithm for properly learning a mixture of k gaussians for any constant k":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures")
  Member "optimal testing for properties of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "the fourier transform of poisson multinomial distributions and its algorithmic applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private hypothesis selection")
  Member "properly learning poisson binomial distributions in almost polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "optimal learning via the fourier transform for sums of independent integer random variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "sample optimal density estimation in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")

Question: Please tell me which class the source paper 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)