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 "universal background sparse coding and multilayer bootstrap network for speaker recognition"):
  Abstract: In speaker recognition, Gaussian mixture model based universal background model is a standard for extracting high-dimensional supervectors, and factor-analysis-based i-vector is a recent state-of-the-art method for reducing the high-dimensional supervectors to low-dimensional representations. In this abstract paper, we propose an alternative to the aforementioned techniques by multilayer bootstrap networks (MBN). We first learn a high-dimensional sparse code for each frame by a universal background MBN, and then accumulate the sparse codes of the frames in a session (a.k.a. utterance) into a single high-dimensional sparse supervector. Finally, we reduce the session-level sparse supervectors to a low-dimensional subspace by MBN for unsupervised speaker clustering, or principle component analysis for supervised speaker classification. Our initial result on a small-scale problem demonstrates the effectiveness of the proposed method. #R##N#Note that this abstract paper is used to protect the idea. A full version with large-scale experiments will be announced later.

Includes members:
  Member "multilayer bootstrap network for unsupervised speaker recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural predictive coding using convolutional neural networks toward unsupervised learning of speaker characteristics")
  Member "multilayer bootstrap networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel speech separation using deep embedding model with multilayer bootstrap networks")
  Member "nonlinear dimensionality reduction of data by deep distributed random samplings":
    also connected through other hyperedges:
      (none)

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.SD(Sound)