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: "sparsely aggregated convolutional networks"
  Abstract: We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers. Such aggregation is critical to facilitate training of very deep networks in an end-to-end manner. This is a primary reason for the widespread adoption of residual networks, which aggregate outputs via cumulative summation. While subsequent works investigate alternative aggregation operations (e.g. concatenation), we focus on an orthogonal question: which outputs to aggregate at a particular point in the network. We propose a new internal connection structure which aggregates only a sparse set of previous outputs at any given depth. Our experiments demonstrate this simple design change offers superior performance with fewer parameters and lower computational requirements. Moreover, we show that sparse aggregation allows networks to scale more robustly to 1000+ layers, thereby opening future avenues for training long-running visual processes.

Connected through hyperedge HE-1 (induced by source paper "cspnet a new backbone that can enhance learning capability of cnn"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "thundernet towards real time generic object detection"
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "objects as points"
    - "hardnet a low memory traffic network"
    - "cornernet lite efficient keypoint based object detection"
    - "freeanchor learning to match anchors for visual object detection"

Connected through hyperedge HE-2 (induced by source paper "sparsemask differentiable connectivity learning for dense image prediction"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "maskconnect connectivity learning by gradient descent"
    - "context encoding for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "regularized evolution for image classifier architecture search"
    - "neural arithmetic logic units"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "making neural programming architectures generalize via recursion"
    - "deep expander networks efficient deep networks from graph theory"

Connected through hyperedge HE-4 (induced by source paper "gated convolutional networks with hybrid connectivity for image classification"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "log densenet how to sparsify a densenet"
    - "dropblock a regularization method for convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "mixed link networks"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-5 (induced by source paper "alert accurate anytime learning for energy and timeliness"):
  with members:
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "anytime stereo image depth estimation on mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "hardnet a low memory traffic network"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "scale sim systolic cnn accelerator simulator"
    - "single shot refinement neural network for object detection"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-7 (induced by source paper "an energy and gpu computation efficient backbone network for real time object detection"):
  with members:
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "constructing fast network through deconstruction of convolution"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "proxylessnas direct neural architecture search on target task and hardware"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "neural architecture construction using envelopenets"
    - "amc automl for model compression and acceleration on mobile devices"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "neural architecture optimization"
    - "monas multi objective neural architecture search using reinforcement 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)