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: "improved selective refinement network for face detection"
  Abstract: As a long-standing problem in computer vision, face detection has attracted much attention in recent decades for its practical applications. With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years. Among them, the Selective Refinement Network (SRN) face detector introduces the two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. Moreover, it designs a receptive field enhancement block to provide more diverse receptive field. In this report, to further improve the performance of SRN, we exploit some existing techniques via extensive experiments, including new data augmentation strategy, improved backbone network, MS COCO pretraining, decoupled classification module, segmentation branch and Squeeze-and-Excitation block. Some of these techniques bring performance improvements, while few of them do not well adapt to our baseline. As a consequence, we present an improved SRN face detector by combining these useful techniques together and obtain the best performance on widely used face detection benchmark WIDER FACE dataset.

Connected through hyperedge HE-1 (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"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-2 (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"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (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"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "retinaface single stage dense face localisation in the wild"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "dsfd dual shot face detector"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "lffd a light and fast face detector for edge devices"):
  with members:
    - "robust and high performance face detector"
    - "dsfd dual shot face detector"
    - "seeing small faces from robust anchor s perspective"
    - "yolov3 an incremental improvement"
    - "deep face recognition a survey"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "casia surf a large scale multi modal benchmark for face anti spoofing"):
  with members:
    - "refineface refinement neural network for high performance face detection"
    - "mis classified vector guided softmax loss for face recognition"
    - "face de spoofing anti spoofing via noise modeling"
    - "ensemble soft margin softmax loss for image classification"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "selective refinement network for high performance face detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "support vector guided softmax loss for face recognition"

Connected through hyperedge HE-8 (induced by source paper "learning better features for face detection with feature fusion and segmentation supervision"):
  with members:
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "face detection through scale friendly deep convolutional networks"
    - "understanding the effective receptive field in deep convolutional neural 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)