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 "convex relaxations of bregman divergence clustering"):
  Abstract: Although many convex relaxations of clustering have been proposed in the past decade, current formulations remain restricted to spherical Gaussian or discriminative models and are susceptible to imbalanced clusters. To address these shortcomings, we propose a new class of convex relaxations that can be flexibly applied to more general forms of Bregman divergence clustering. By basing these new formulations on normalized equivalence relations we retain additional control on relaxation quality, which allows improvement in clustering quality. We furthermore develop optimization methods that improve scalability by exploiting recent implicit matrix norm methods. In practice, we find that the new formulations are able to efficiently produce tighter clusterings that improve the accuracy of state of the art methods.

Includes members:
  Member "a hybrid algorithm for convex semidefinite optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear factorization for low rank sdp learning")
  Member "k mle a fast algorithm for learning statistical mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions")
  Member "a convex relaxation for weakly supervised classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "more supervision less computation statistical computational tradeoffs in weakly supervised learning")
  Member "learning mixtures of spherical gaussians moment methods and spectral decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable meta learning of linear representations")
  Member "settling the polynomial learnability of mixtures of gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "learning mixtures of gaussians using the k means algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the analysis of em for truncated mixtures of two 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.LG(Machine Learning)