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: "pooling pyramid network for object detection"
  Abstract: We'd like to share a simple tweak of Single Shot Multibox Detector (SSD) family of detectors, which is effective in reducing model size while maintaining the same quality. We share box predictors across all scales, and replace convolution between scales with max pooling. This has two advantages over vanilla SSD: (1) it avoids score miscalibration across scales; (2) the shared predictor sees the training data over all scales. Since we reduce the number of predictors to one, and trim all convolutions between them, model size is significantly smaller. We empirically show that these changes do not hurt model quality compared to vanilla SSD.

Connected through hyperedge HE-1 (induced by source paper "the indirect convolution algorithm"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "panoptic feature pyramid networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "high performance zero memory overhead direct convolutions"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "le hgr a lightweight and efficient rgb based online gesture recognition network for embedded ar devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "memory attention networks for skeleton based action recognition"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "convolutional pose machines"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "blazeface sub millisecond neural face detection on mobile gpus"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ssd single shot multibox detector"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal 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)