human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "text independent speaker verification using 3d convolutional neural networks"
  Abstract: In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN) architecture has been proposed for speaker verification in the text-independent setting. One of the main challenges is the creation of the speaker models. Most of the previously-reported approaches create speaker models based on averaging the extracted features from utterances of the speaker, which is known as the d-vector system. In our paper, we propose an adaptive feature learning by utilizing the 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speakers' utterances and creation of the speaker model. This leads to simultaneously capturing the speaker-related information and building a more robust system to cope with within-speaker variation. We demonstrate that the proposed method significantly outperforms the traditional d-vector verification system. Moreover, the proposed system can also be an alternative to the traditional d-vector system which is a one-shot speaker modeling system by utilizing 3D-CNNs.

Connected through hyperedge HE-1 (induced by source paper "lossless compression for 3dcnns based on tensor train decomposition"):
  with members:
    - "tensor train decomposition on tensorflow t3f"
    - "tensor decomposition for compressing recurrent neural network"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "expressive power of recurrent neural networks"
    - "deformable convolutional networks"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "tensor train recurrent neural networks for video classification"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-2 (induced by source paper "sparse architectures for text independent speaker verification using deep neural networks"):
  with members:
    - "attention based guided structured sparsity of deep neural networks"
    - "speechpy a library for speech processing and recognition"
    - "polar coding for achieving the capacity of marginal channels in nonbinary input setting"
    - "voxceleb a large scale speaker identification dataset"
    - "3d convolutional neural networks for cross audio visual matching recognition"
    - "on the construction of polar codes for achieving the capacity of marginal channels"
    - "learning structured sparsity in deep neural networks"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-3 (induced by source paper "speechpy a library for speech processing and recognition"):
  with members:
    - "attention based guided structured sparsity of deep neural networks"
    - "3d convolutional neural networks for cross audio visual matching recognition"

Connected through hyperedge HE-4 (induced by source paper "3d convolutional neural networks for cross audio visual matching recognition"):
  with members:
    - "lipreading with long short term memory"
    - "deep multimodal learning for audio visual speech recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "lip localization and viseme classification for visual speech recognition"
    - "building high level features using large scale unsupervised learning"

Question: Please tell me which class the center node 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.CV(Computer Vision and Pattern Recognition)