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: "real time rotation invariant face detection with progressive calibration networks"
  Abstract: Rotation-invariant face detection, i.e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances. Most existing methods compromise with speed or accuracy to handle the large RIP variations. To address this problem more efficiently, we propose Progressive Calibration Networks (PCN) to perform rotation-invariant face detection in a coarse-to-fine manner. PCN consists of three stages, each of which not only distinguishes the faces from non-faces, but also calibrates the RIP orientation of each face candidate to upright progressively. By dividing the calibration process into several progressive steps and only predicting coarse orientations in early stages, PCN can achieve precise and fast calibration. By performing binary classification of face vs. non-face with gradually decreasing RIP ranges, PCN can accurately detect faces with full $360^{\circ}$ RIP angles. Such designs lead to a real-time rotation-invariant face detector. The experiments on multi-oriented FDDB and a challenging subset of WIDER FACE containing rotated faces in the wild show that our PCN achieves quite promising performance.

Connected through hyperedge HE-1 (induced by source paper "arbitrary oriented object detection with circular smooth label"):
  with members:
    - "objects detection for remote sensing images based on polar coordinates"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "ienet interacting embranchment one stage anchor free detector for orientation aerial object detection"
    - "centernet keypoint triplets for object detection"
    - "adaptive period embedding for representing oriented objects in aerial images"
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "gliding vertex on the horizontal bounding box for multi oriented object detection"
    - "cad net a context aware detection network for objects in remote sensing imagery"

Connected through hyperedge HE-2 (induced by source paper "improved selective refinement network for face detection"):
  with members:
    - "robust and high performance face detector"
    - "rethinking imagenet pre training"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "group normalization"
    - "fa rpn floating region proposals for face detection"
    - "pyramidbox a context assisted single shot face detector"
    - "scratchdet exploring to train single shot object detectors from scratch"

Connected through hyperedge HE-3 (induced by source paper "rotated feature network for multi orientation object detection"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "edit probability for scene text recognition"
    - "multi scale location aware kernel representation for object detection"
    - "focal loss for dense object detection"
    - "oriented response networks"
    - "squeeze and excitation networks"
    - "deep direct regression for multi oriented scene text detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-4 (induced by source paper "detecting lesion bounding ellipses with gaussian proposal networks"):
  with members:
    - "bounding box regression with uncertainty for accurate object detection"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "towards the success rate of one real time unconstrained salient object detection"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "mask r cnn"
    - "learning a rotation invariant detector with rotatable bounding box"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "lidartag a real time fiducial tag using point clouds"):
  with members:
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "rotation equivariant vector field networks"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "3d fully convolutional network for vehicle detection in point cloud"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"

Connected through hyperedge HE-6 (induced by source paper "face detection in camera captured images of identity documents under challenging conditions"):
  with members:
    - "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"
    - "docface matching id document photos to selfies"
    - "ssh single stage headless face detector"
    - "grid loss detecting occluded faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "wider face a face detection benchmark"
    - "from facial parts responses to face detection a deep learning approach"

Connected through hyperedge HE-7 (induced by source paper "selective refinement network for high performance face detection"):
  with members:
    - "pyramidbox a context assisted single shot face detector"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "cascade r cnn delving into high quality object detection"
    - "detecting faces using region based fully convolutional networks"
    - "scale aware face detection"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-8 (induced by source paper "rotation ensemble module for detecting rotation invariant features"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "polar transformer networks"
    - "robotic grasp detection using deep convolutional neural networks"
    - "inverse compositional spatial transformer networks"
    - "group equivariant convolutional networks"
    - "spatial transformer networks"
    - "multi scale context aggregation by dilated convolutions"
    - "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)