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: "blockout dynamic model selection for hierarchical deep networks"
  Abstract: Most deep architectures for image classification–even those that are trained to classify a large number of diverse categories–learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very different. While hierarchical deep networks address this problem by learning separate features for subsets of related categories, current implementations require simplified models using fixed architectures specified via heuristic clustering methods. Instead, we propose Blockout, a method for regularization and model selection that simultaneously learns both the model architecture and parameters. A generalization of Dropout, our approach gives a novel parametrization of hierarchical architectures that allows for structure learning via back-propagation. To demonstrate its utility, we evaluate Blockout on the CIFAR and Image Net datasets, demonstrating improved classification accuracy, better regularization performance, faster training, and the clear emergence of hierarchical network structures.

Connected through hyperedge HE-1 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-2 (induced by source paper "3d point cloud generative adversarial network based on tree structured graph convolutions"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "multiresolution tree networks for 3d point cloud processing"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "conditional image to image translation"
    - "dynamic graph cnn for learning on point clouds"
    - "vital visual tracking via adversarial learning"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-3 (induced by source paper "learning with hierarchical complement objective"):
  with members:
    - "complement objective training"
    - "improving adversarial robustness via guided complement entropy"
    - "do convolutional neural networks learn class hierarchy"
    - "dilated residual networks"
    - "mixup beyond empirical risk minimization"
    - "improved regularization of convolutional neural networks with cutout"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "conditional information gain networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "deep convolutional decision jungle for image classification"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decision forests convolutional networks and the models in between"

Connected through hyperedge HE-5 (induced by source paper "a hierarchical matcher using local classifier chains"):
  with members:
    - "patch based face recognition using a hierarchical multi label matcher"
    - "a face recognition signature combining patch based features with soft facial attributes"
    - "fully associative patch based 1 to n matcher for face recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "identity mappings in deep residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deepid3 face recognition with very deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "connectivity learning in multi branch networks"):
  with members:
    - "branchconnect image categorization with learned branch connections"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "shake shake regularization"
    - "convolutional neural fabrics"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "learning end to end multimodal sensor policies for autonomous navigation"):
  with members:
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "sensor fusion for robot control through deep reinforcement learning"
    - "end to end learning for self driving cars"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-8 (induced by source paper "n2n learning network to network compression via policy gradient reinforcement learning"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"
    - "all you need is a good init"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural 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)