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: "thundernet towards real time generic object detection"
  Abstract: Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.

Connected through hyperedge HE-1 (induced by source paper "bidet an efficient binarized object detector"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "embarrassingly simple binary representation learning"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "importance estimation for neural network pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "efficient neural network compression"
    - "private model compression via knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "extd extremely tiny face detector via iterative filter reuse"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "improved selective refinement network for face detection"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "dsfd dual shot face detector"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-3 (induced by source paper "sm nas structural to modular neural architecture search for object detection"):
  with members:
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "revisiting feature alignment for one stage object detection"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "cspnet a new backbone that can enhance learning capability of cnn"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "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"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "mnasfpn learning latency aware pyramid architecture for object detection on mobile devices"):
  with members:
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "panoptic feature pyramid networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "detnas backbone search for object detection"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "differentiable architecture search with ensemble gumbel softmax"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "rethinking imagenet pre training"

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)