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: "adascale towards real time video object detection using adaptive scaling"
  Abstract: In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation. The key insight we show in this paper is that speed and accuracy are not necessarily a trade-off when it comes to image scaling. Our results show that re-scaling the image to a lower resolution will sometimes produce better accuracy. Based on this observation, we propose a novel approach, dubbed AdaScale, which adaptively selects the input image scale that improves both accuracy and speed for video object detection. To this end, our results on ImageNet VID and mini YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP improvement with 1.6x and 1.8x speedup, respectively. Additionally, we improve state-of-the-art video acceleration work by an extra 1.25x speedup with slightly better mAP on ImageNet VID dataset.

Connected through hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "importance estimation for neural network pruning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mavbench micro aerial vehicle benchmarking"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"

Connected through hyperedge HE-2 (induced by source paper "mutualnet adaptive convnet via mutual learning from network width and resolution"):
  with members:
    - "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"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-3 (induced by source paper "e2 train training state of the art cnns with over 80 energy savings"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "swalp stochastic weight averaging in low precision training"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-4 (induced by source paper "legr filter pruning via learned global ranking"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "transformer xl attentive language models beyond a fixed length context"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "habitat a platform for embodied ai research"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"

Connected through hyperedge HE-5 (induced by source paper "it s all about the scale efficient text detection using adaptive scaling"):
  with members:
    - "character region awareness for text detection"
    - "pyramid mask text detector"
    - "shape robust text detection with progressive scale expansion network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "autofocus efficient multi scale inference"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"

Connected through hyperedge HE-6 (induced by source paper "regularizing activation distribution for training binarized deep networks"):
  with members:
    - "understanding the impact of label granularity on cnn based image classification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "intelligence beyond the edge inference on intermittent embedded systems"
    - "self normalizing neural networks"

Connected through hyperedge HE-7 (induced by source paper "a closer look at network resolution for efficient network design"):
  with members:
    - "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"
    - "slimmable neural networks"

Connected through hyperedge HE-8 (induced by source paper "to what extent does downsampling compression and data scarcity impact renal image analysis"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "feature pyramid networks for object detection"

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)