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: "nestednet learning nested sparse structures in deep neural networks"
  Abstract: Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep architecture for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested sparse network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.

Connected through hyperedge HE-1 (induced by source paper "mutualnet adaptive convnet via mutual learning from network width and resolution"):
  with members:
    - "adascale towards real time video object detection using adaptive scaling"
    - "universally slimmable networks and improved training techniques"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fast autoaugment"
    - "deep high resolution representation learning for human pose estimation"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "fractional skipping towards finer grained dynamic cnn inference"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "slimmable neural networks"
    - "hybrid pruning thinner sparse networks for fast inference on edge devices"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "haq hardware aware automated quantization with mixed precision"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-3 (induced by source paper "dual dynamic inference enabling more efficient adaptive and controllable deep inference"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "slimmable neural networks"
    - "hybrid pruning thinner sparse networks for fast inference on edge devices"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"
    - "deep layer aggregation"

Connected through hyperedge HE-4 (induced by source paper "fishnet a versatile backbone for image region and pixel level prediction"):
  with members:
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "convolutional neural networks with alternately updated clique"
    - "i revnet deep invertible networks"
    - "question guided hybrid convolution for visual question answering"
    - "deep layer aggregation"
    - "learning feature pyramids for human pose estimation"
    - "dilated residual networks"
    - "multi scale dense networks for resource efficient image classification"

Connected through hyperedge HE-5 (induced by source paper "deep virtual networks for memory efficient inference of multiple tasks"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "convolutional networks with adaptive inference graphs"
    - "continual learning through synaptic intelligence"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-6 (induced by source paper "deep elastic networks with model selection for multi task learning"):
  with members:
    - "deep virtual networks for memory efficient inference of multiple tasks"
    - "multi task zipping via layer wise neuron sharing"
    - "maskconnect connectivity learning by gradient descent"
    - "a novel channel pruning method for deep neural network compression"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "focal loss for dense object detection"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-7 (induced by source paper "a closer look at network resolution for efficient network design"):
  with members:
    - "adascale towards real time video object detection using adaptive scaling"
    - "universally slimmable networks and improved training techniques"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "deep high resolution representation learning for human pose estimation"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-8 (induced by source paper "biologically inspired spatial neural networks"):
  with members:
    - "explaining explanations an overview of interpretability of machine learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "learning the number of neurons in deep 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)