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: "3d convolutional neural networks for cross audio visual matching recognition"
  Abstract: Audio–visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this paper. We propose the use of a coupled 3D convolutional neural network (3D CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio–visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities. By using a relatively small network architecture and much smaller data set for training, our proposed method surpasses the performance of the existing similar methods for audio–visual matching, which use 3D CNNs for feature representation. We also demonstrate that an effective pair selection method can significantly increase the performance. The proposed method achieves relative improvements over 20% on the equal error rate and over 7% on the average precision in comparison to the state-of-the-art method.

Connected through hyperedge HE-1 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "audio visual event localization in unconstrained videos"
    - "learnable pins cross modal embeddings for person identity"
    - "git loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "on learning associations of faces and voices"
    - "look listen and learn"

Connected through hyperedge HE-2 (induced by source paper "large scale visual speech recognition"):
  with members:
    - "deep audio visual speech recognition"
    - "the conversation deep audio visual speech enhancement"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improving speaker independent lipreading with domain adversarial training"
    - "lip2audspec speech reconstruction from silent lip movements video"

Connected through hyperedge HE-3 (induced by source paper "an empirical analysis of deep audio visual models for speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "the conversation deep audio visual speech enhancement"
    - "large scale visual speech recognition"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "end to end audiovisual speech recognition"
    - "mnasnet platform aware neural architecture search for mobile"
    - "single image super resolution via a lightweight residual convolutional neural network"

Connected through hyperedge HE-4 (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"
    - "text independent speaker verification using 3d convolutional neural networks"
    - "polar coding for achieving the capacity of marginal channels in nonbinary input setting"
    - "voxceleb a large scale speaker identification dataset"
    - "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-5 (induced by source paper "deep sketch photo face recognition assisted by facial attributes"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "face attribute prediction using off the shelf cnn features"
    - "sparse graphical representation based discriminant analysis for heterogeneous face recognition"
    - "moon a mixed objective optimization network for the recognition of facial attributes"

Connected through hyperedge HE-6 (induced by source paper "a bimodal learning approach to assist multi sensory effects synchronization"):
  with members:
    - "very deep convolutional neural networks for raw waveforms"
    - "soundnet learning sound representations from unlabeled video"
    - "tensorflow a system for large scale machine learning"
    - "deep multimodal speaker naming"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-7 (induced by source paper "speechpy a library for speech processing and recognition"):
  with members:
    - "attention based guided structured sparsity of deep neural networks"
    - "text independent speaker verification using 3d convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "text independent speaker verification using 3d convolutional neural networks"):
  with members:
    - "speechpy a library for speech processing and recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)