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: "extd extremely tiny face detector via iterative filter reuse"
  Abstract: In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD),less than 0.1 million, as well as achieving comparable performance to deep heavy detectors. While existing multi-scale face detectors extract feature maps with different scales from a single backbone network, our method generates the feature maps by iteratively reusing a shared lightweight and shallow backbone network. This iterative sharing of the backbone network significantly reduces the number of parameters, and also provides the abstract image semantics captured from the higher stage of the network layers to the lower-level feature map. The proposed idea is employed by various model architectures and evaluated by extensive experiments. From the experiments from WIDER FACE dataset, we show that the proposed face detector can handle faces with various scale and conditions, and achieved comparable performance to the more massive face detectors that few hundreds and tens times heavier in model size and floating point operations.

Connected through hyperedge HE-1 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference"):
  with members:
    - "visual wake words dataset"
    - "lffd a light and fast face detector for edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-2 (induced by source paper "face detection with feature pyramids and landmarks"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "robust and high performance face detector"
    - "self driving cars a survey"
    - "refineface refinement neural network for high performance face detection"
    - "searching for mobilenetv3"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"

Connected through hyperedge HE-3 (induced by source paper "extremec3net extreme lightweight portrait segmentation networks using advanced c3 modules"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "character region awareness for text detection"
    - "fast scnn fast semantic segmentation network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "c3 concentrated comprehensive convolution and its application to semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "sinet extreme lightweight portrait segmentation networks with spatial squeeze modules and information blocking decoder"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "searching for mobilenetv3"
    - "hbonet harmonious bottleneck on two orthogonal dimensions"
    - "fast scnn fast semantic segmentation network"
    - "elastic improving cnns with dynamic scaling policies"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "c3 concentrated comprehensive convolution and its application to semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"

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)