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: "context aware single shot detector"
  Abstract: SSD is one of the state-of-the-art object detection algorithms, and it combines high detection accuracy with real-time speed. However, it is widely recognized that SSD is less accurate in detecting small objects compared to large objects, because it ignores the context from outside the proposal boxes. In this paper, we present CSSD--a shorthand for context-aware single-shot multibox object detector. CSSD is built on top of SSD, with additional layers modeling multi-scale contexts. We describe two variants of CSSD, which differ in their context layers, using dilated convolution layers (DiCSSD) and deconvolution layers (DeCSSD) respectively. The experimental results show that the multi-scale context modeling significantly improves the detection accuracy. In addition, we study the relationship between effective receptive fields (ERFs) and the theoretical receptive fields (TRFs), particularly on a VGGNet. The empirical results further strengthen our conclusion that SSD coupled with context layers achieves better detection results especially for small objects ($+3.2\% {\rm AP}_{@0.5}$ on MS-COCO compared to the newest SSD), while maintaining comparable runtime performance.

Connected through hyperedge HE-1 (induced by source paper "liver lesion detection from weakly labeled multi phase ct volumes with a grouped single shot multibox detector"):
  with members:
    - "feature fused ssd fast detection for small objects"
    - "fssd feature fusion single shot multibox detector"
    - "liver lesion segmentation informed by joint liver segmentation"
    - "automatic liver lesion detection using cascaded deep residual networks"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "densely connected convolutional networks"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"

Connected through hyperedge HE-2 (induced by source paper "stairnet top down semantic aggregation for accurate one shot detection"):
  with members:
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "couplenet coupling global structure with local parts for object detection"
    - "dssd deconvolutional single shot detector"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "accurate single stage detector using recurrent rolling convolution"
    - "stacked hourglass networks for human pose estimation"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "fhedn a based on context modeling feature hierarchy encoder decoder network for face detection"):
  with members:
    - "dssd deconvolutional single shot detector"
    - "detecting faces using region based fully convolutional networks"
    - "face detection through scale friendly deep convolutional networks"
    - "ssh single stage headless face detector"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"

Connected through hyperedge HE-4 (induced by source paper "weaving multi scale context for single shot detector"):
  with members:
    - "stairnet top down semantic aggregation for accurate one shot detection"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "dual path networks"
    - "accurate single stage detector using recurrent rolling convolution"
    - "yolo9000 better faster stronger"
    - "hypernet towards accurate region proposal generation and joint 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)