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: "dsfd dual shot face detector"
  Abstract: In this paper, we propose a novel face detection network with three novel contributions that address three key aspects of face detection, including better feature learning, progressive loss design and anchor assign based data augmentation, respectively. First, we propose a Feature Enhance Module (FEM) for enhancing the original feature maps to extend the single shot detector to dual shot detector. Second, we adopt Progressive Anchor Loss (PAL) computed by two different sets of anchors to effectively facilitate the features. Third, we use an Improved Anchor Matching (IAM) by integrating novel anchor assign strategy into data augmentation to provide better initialization for the regressor. Since these techniques are all related to the two-stream design, we name the proposed network as Dual Shot Face Detector (DSFD). Extensive experiments on popular benchmarks, WIDER FACE and FDDB, demonstrate the superiority of DSFD over the state-of-the-art face detectors.

Connected through hyperedge HE-1 (induced by source paper "kpnet towards minimal face detector"):
  with members:
    - "bottom up object detection by grouping extreme and center points"
    - "pfld a practical facial landmark detector"
    - "look at boundary a boundary aware face alignment algorithm"
    - "cornernet detecting objects as paired keypoints"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "gradient harmonized single stage detector"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-2 (induced by source paper "zero reference deep curve estimation for low light image enhancement"):
  with members:
    - "enlightengan deep light enhancement without paired supervision"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "the perception distortion tradeoff"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "wider face a face detection benchmark"

Connected through hyperedge HE-3 (induced by source paper "retinaface single stage dense face localisation in the wild"):
  with members:
    - "robust and high performance face detector"
    - "on learning 3d face morphable model from in the wild images"
    - "wider face and pedestrian challenge 2018 methods and results"
    - "improved selective refinement network for face detection"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "face detection using improved faster rcnn"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-4 (induced by source paper "refineface refinement neural network for high performance face detection"):
  with members:
    - "robust and high performance face detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"
    - "improved selective refinement network for face detection"
    - "face detection using improved faster rcnn"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-5 (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"
    - "thundernet towards real time generic object detection"
    - "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"
    - "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-6 (induced by source paper "improved selective refinement network for face detection"):
  with members:
    - "robust and high performance face detector"
    - "rethinking imagenet pre training"
    - "face detection using improved faster rcnn"
    - "group normalization"
    - "fa rpn floating region proposals for face detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "pyramidbox a context assisted single shot face detector"
    - "scratchdet exploring to train single shot object detectors from scratch"

Connected through hyperedge HE-7 (induced by source paper "high level semantic feature detection a new perspective for pedestrian detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "citypersons a diverse dataset for pedestrian detection"

Connected through hyperedge HE-8 (induced by source paper "accurate face detection for high performance"):
  with members:
    - "robust and high performance face detector"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "pyramidbox high performance detector for finding tiny face"
    - "improved selective refinement network for face detection"
    - "face detection using improved faster rcnn"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"
    - "learning better features for face detection with feature fusion and segmentation supervision"

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)