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: "deep networks with internal selective attention through feedback connections"
  Abstract: Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNets feedback structure can dynamically alter its convolutional filter sensitivities during classification. It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters. Feedback is trained through direct policy search in a huge million-dimensional parameter space, through scalable natural evolution strategies (SNES). On the CIFAR-10 and CIFAR-100 datasets, dasNet outperforms the previous state-of-the-art model.

Connected through hyperedge HE-1 (induced by source paper "feedback graph convolutional network for skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "feedback network for image super resolution"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "interaction relational network for mutual action recognition"
    - "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"

Connected through hyperedge HE-2 (induced by source paper "context gated convolution"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "attention augmented convolutional networks"
    - "generating long sequences with sparse transformers"
    - "weight standardization"
    - "eco efficient convolutional network for online video understanding"
    - "bam bottleneck attention module"

Connected through hyperedge HE-3 (induced by source paper "inner imaging networks put lenses into convolutional structure"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "convolutional neural networks with alternately updated clique"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "training neural networks using features replay"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "multi modal attention network learning for semantic source code retrieval"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "multimodal dual attention memory for video story question answering"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-5 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "modular networks learning to decompose neural computation"
    - "learning abstract options"
    - "evolutionary architecture search for deep multitask networks"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-6 (induced by source paper "inspect transfer learning architecture with dilated convolution"):
  with members:
    - "a survey on deep transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "drop activation implicit parameter reduction and harmonic regularization"
    - "deformable convolutional networks"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-7 (induced by source paper "pdanet polarity consistent deep attention network for fine grained visual emotion regression"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"
    - "cbam convolutional block attention module"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "squeeze and excitation networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-8 (induced by source paper "feedback network for image super resolution"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"

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)