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 "an open source c implementation of multi threaded gaussian mixture models k means and expectation maximisation"):
  Abstract: Modelling of multivariate densities is a core component in many signal processing, pattern recognition and machine learning applications. The modelling is often done via Gaussian mixture models (GMMs), which use computationally expensive and potentially unstable training algorithms. We provide an overview of a fast and robust implementation of GMMs in the C++ language, employing multi-threaded versions of the Expectation Maximisation (EM) and k-means training algorithms. Multi-threading is achieved through reformulation of the EM and k-means algorithms into a MapReduce-like framework. Furthermore, the implementation uses several techniques to improve numerical stability and modelling accuracy. We demonstrate that the multi-threaded implementation achieves a speedup of an order of magnitude on a recent 16 core machine, and that it can achieve higher modelling accuracy than a previously well-established publically accessible implementation. The multi-threaded implementation is included as a user-friendly class in recent releases of the open source Armadillo C++ linear algebra library. The library is provided under the permissive Apache~2.0 license, allowing unencumbered use in commercial products.

Includes members:
  Member "exploiting temporal information for dcnn based fine grained object classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chest x rays classification a multi label and fine grained problem")
  Member "comparative evaluation of action recognition methods via riemannian manifolds fisher vectors and gmms ideal and challenging conditions":
    also connected through other hyperedges:
      (none)
  Member "modelling local deep convolutional neural network features to improve fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aquasight automatic water impurity detection utilizing convolutional neural networks")
  Member "on robust face recognition via sparse coding the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "log euclidean bag of words for human action recognition")
  Member "improved foreground detection via block based classifier cascade with probabilistic decision integration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi action recognition via stochastic modelling of optical flow and gradients")
  Member "mlpack a scalable c machine learning library":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on explaining machine learning models by evolving crucial and compact features")
  Member "revisiting k means new algorithms via bayesian nonparametrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "infinite mixture prototypes for few shot learning")
  Member "the pitfalls of verifying floating point computations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abstract compilation for verification of numerical accuracy properties")

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.MS(Mathematical Software)