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: "deeply coupled auto encoder networks for cross view classification"
  Abstract: The comparison of heterogeneous samples extensively exists in many applications, especially in the task of image classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers. In DCAN, each deep structure is developed via stacking multiple discriminative coupled auto-encoders, a denoising auto-encoder trained with maximum margin criterion consisting of intra-class compactness and inter-class penalty. This single layer component makes our model simultaneously preserve the local consistency and enhance its discriminative capability. With increasing number of layers, the coupled networks can gradually narrow the gap between the two views. Extensive experiments on cross-view image classification tasks demonstrate the superiority of our method over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "m 2 deep id a novel model for multi view face identification using convolutional deep neural networks"):
  with members:
    - "deep face recognition a survey"
    - "theano a python framework for fast computation of mathematical expressions"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "dense multimodal fusion for hierarchically joint representation"):
  with members:
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "multimodal machine learning a survey and taxonomy"
    - "deep multimodal representation learning from temporal data"
    - "deep binary reconstruction for cross modal hashing"
    - "a comprehensive survey on cross modal retrieval"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "moddrop adaptive multi modal gesture recognition"

Connected through hyperedge HE-3 (induced by source paper "motion blur removal via coupled autoencoder"):
  with members:
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "on vectorization of deep convolutional neural networks for vision tasks"
    - "learning a convolutional neural network for non uniform motion blur removal"

Connected through hyperedge HE-4 (induced by source paper "semi coupled two stream fusion convnets for action recognition at extremely low resolutions"):
  with members:
    - "privacy preserving human activity recognition from extreme low resolution"
    - "convolutional two stream network fusion for video action recognition"
    - "studying very low resolution recognition using deep networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "pooled motion features for first person videos"
    - "matconvnet convolutional neural networks for matlab"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "studying very low resolution recognition using deep networks"):
  with members:
    - "synthesis based robust low resolution face recognition"
    - "real world font recognition using deep network and domain adaptation"
    - "self tuned deep super resolution"
    - "learning super resolution jointly from external and internal examples"
    - "deepfont identify your font from an image"
    - "deep networks for image super resolution with sparse prior"

Connected through hyperedge HE-6 (induced by source paper "a comprehensive survey on pose invariant face recognition"):
  with members:
    - "multi view intact space learning"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "robust face recognition via multimodal deep face representation"
    - "facenet a unified embedding for face recognition and clustering"
    - "facial feature point detection a comprehensive survey"
    - "effective face frontalization in unconstrained images"
    - "recover canonical view faces in the wild with deep neural networks"
    - "web scale training for face identification"

Connected through hyperedge HE-7 (induced by source paper "freehand sketch recognition using deep features"):
  with members:
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "learning from synthetic data using a stacked multichannel autoencoder"):
  with members:
    - "marginalized denoising autoencoders for domain adaptation"
    - "feature hashing for large scale multitask 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)