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 "robustly clustering a mixture of gaussians"):
  Abstract: We give an efficient algorithm for robustly clustering of a mixture of arbitrary Gaussians, a central open problem in the theory of computationally efficient robust estimation, assuming only that for each pair of component Gaussians, their means are well-separated or their covariances are well-separated.

Includes members:
  Member "efficient algorithms and lower bounds for robust linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "mixture models robustness and sum of squares proofs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning for mixed linear regression")
  Member "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "agnostic estimation of mean and covariance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "robust estimators in high dimensions without the computational intractability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "robust regression via hard thresholding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "on the complexity of random satisfiability problems with planted solutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion")
  Member "learning mixtures of spherical gaussians moment methods and spectral decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")

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)