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 "list decodable robust mean estimation and learning mixtures of spherical gaussians"):
  Abstract: We study the problem of list-decodable Gaussian mean estimation and the related problem of learning mixtures of separated spherical Gaussians. We develop a set of techniques that yield new efficient algorithms with significantly improved guarantees for these problems. #R##N#{\bf List-Decodable Mean Estimation.} Fix any $d \in \mathbb{Z}_+$ and $0< \alpha <1/2$. We design an algorithm with runtime $O (\mathrm{poly}(n/\alpha)^{d})$ that outputs a list of $O(1/\alpha)$ many candidate vectors such that with high probability one of the candidates is within $\ell_2$-distance $O(\alpha^{-1/(2d)})$ from the true mean. The only previous algorithm for this problem achieved error $\tilde O(\alpha^{-1/2})$ under second moment conditions. For $d = O(1/\epsilon)$, our algorithm runs in polynomial time and achieves error $O(\alpha^{\epsilon})$. We also give a Statistical Query lower bound suggesting that the complexity of our algorithm is qualitatively close to best possible. #R##N#{\bf Learning Mixtures of Spherical Gaussians.} We give a learning algorithm for mixtures of spherical Gaussians that succeeds under significantly weaker separation assumptions compared to prior work. For the prototypical case of a uniform mixture of $k$ identity covariance Gaussians we obtain: For any $\epsilon>0$, if the pairwise separation between the means is at least $\Omega(k^{\epsilon}+\sqrt{\log(1/\delta)})$, our algorithm learns the unknown parameters within accuracy $\delta$ with sample complexity and running time $\mathrm{poly} (n, 1/\delta, (k/\epsilon)^{1/\epsilon})$. The previously best known polynomial time algorithm required separation at least $k^{1/4} \mathrm{polylog}(k/\delta)$. #R##N#Our main technical contribution is a new technique, using degree-$d$ multivariate polynomials, to remove outliers from high-dimensional datasets where the majority of the points are corrupted.

Includes members:
  Member "certified defenses for data poisoning attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "learning geometric concepts with nasty noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning halfspaces with massart noise under structured distributions")
  Member "being robust in high dimensions can be practical":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private mean estimation of heavy tailed distributions")
  Member "robustly learning a gaussian getting optimal error efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "mixture models robustness and sum of squares proofs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning for mixed linear regression")
  Member "resilience a criterion for learning in the presence of arbitrary outliers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "on learning mixtures of well separated gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "learning from untrusted data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prophet inequalities with linear correlations and augmentations")

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)