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 "sparse architectures for text independent speaker verification using deep neural networks"):
  Abstract: Network pruning is of great importance due to the elimination of the unimportant weights or features activated due to the network over-parametrization. Advantages of sparsity enforcement include preventing the overfitting and speedup. Considering a large number of parameters in deep architectures, network compression becomes of critical importance due to the required huge amount of computational power. In this work, we impose structured sparsity for speaker verification which is the validation of the query speaker compared to the speaker gallery. We will show that the mere sparsity enforcement can improve the verification results due to the possible initial overfitting in the network.

Includes members:
  Member "attention based guided structured sparsity of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards compact convnets via structure sparsity regularized filter pruning")
  Member "speechpy a library for speech processing and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text independent speaker verification using 3d convolutional neural networks")
  Member "text independent speaker verification using 3d convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression for 3dcnns based on tensor train decomposition")
  Member "polar coding for achieving the capacity of marginal channels in nonbinary input setting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the construction of polar codes for achieving the capacity of marginal channels")
  Member "voxceleb a large scale speaker identification dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "first order motion model for image animation")
  Member "3d convolutional neural networks for cross audio visual matching recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals")
  Member "on the construction of polar codes for achieving the capacity of marginal channels":
    also connected through other hyperedges:
      (none)
  Member "learning structured sparsity in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)