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: "skipnet learning dynamic routing in convolutional networks"
  Abstract: While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient. We exploit this observation by learning to skip convolutional layers on a per-input basis. We introduce SkipNet, a modified residual network, that uses a gating network to selectively skip convolutional blocks based on the activations of the previous layer. We formulate the dynamic skipping problem in the context of sequential decision making and propose a hybrid learning algorithm that combines supervised learning and reinforcement learning to address the challenges of non-differentiable skipping decisions. We show SkipNet reduces computation by 30-90% while preserving the accuracy of the original model on four benchmark datasets and outperforms the state-of-the-art dynamic networks and static compression methods. We also qualitatively evaluate the gating policy to reveal a relationship between image scale and saliency and the number of layers skipped.

Connected through hyperedge HE-1 (induced by source paper "switchable precision neural networks"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "adaptative inference cost with convolutional neural mixture models"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "be your own teacher improve the performance of convolutional neural networks via self distillation"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "searching for accurate binary neural architectures"
    - "auxiliary learning for deep multi task learning"

Connected through hyperedge HE-2 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "depth adaptive transformer"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"

Connected through hyperedge HE-3 (induced by source paper "lightweight residual densely connected convolutional neural network"):
  with members:
    - "denser deep evolutionary network structured representation"
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "residual dense network for image restoration"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "residual dense network for image super resolution"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-4 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "automatic pruning for quantized neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "discrimination aware channel pruning for deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-6 (induced by source paper "parot a practical framework for robust deep neural network training"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "a provable defense for deep residual networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "symbolic execution for deep neural networks"
    - "mixtrain scalable training of formally robust neural networks"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "with friends like these who needs adversaries"

Connected through hyperedge HE-7 (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"
    - "learning efficient convolutional networks through network slimming"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-8 (induced by source paper "selective convolutional network an efficient object detector with ignoring background"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "face attention network an effective face detector for the occluded faces"
    - "deformable convolutional networks"
    - "convolutional networks with adaptive inference graphs"
    - "mask r cnn"
    - "channel pruning for accelerating very deep 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)