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: "grid loss detecting occluded faces"
  Abstract: Detection of partially occluded objects is a challenging computer vision problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of the detection window are occluded, since not every sub-part of the window is discriminative on its own. To address this issue, we propose a novel loss layer for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a convolution layer independently rather than over the whole feature map. This results in parts being more discriminative on their own, enabling the detector to recover if the detection window is partially occluded. By mapping our loss layer back to a regular fully connected layer, no additional computational cost is incurred at runtime compared to standard CNNs. We demonstrate our method for face detection on several public face detection benchmarks and show that our method outperforms regular CNNs, is suitable for realtime applications and achieves state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "real time object pose estimation with pose interpreter networks"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "physics based scene level reasoning for object pose estimation in clutter"

Connected through hyperedge HE-2 (induced by source paper "does generative face completion help face recognition"):
  with members:
    - "geometry aware face completion and editing"
    - "identity preserving face completion for large ocular region occlusion"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "additive margin softmax for face verification"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "segmentation aware convolutional networks using local attention masks"

Connected through hyperedge HE-3 (induced by source paper "detecting lesion bounding ellipses with gaussian proposal networks"):
  with members:
    - "bounding box regression with uncertainty for accurate object detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "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"

Connected through hyperedge HE-4 (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"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "docface matching id document photos to selfies"
    - "ssh single stage headless face detector"
    - "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-5 (induced by source paper "precise box score extract more information from datasets to improve the performance of face detection"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "detecting faces using region based fully convolutional networks"
    - "ssh single stage headless face detector"
    - "face r cnn"
    - "face detection with the faster r cnn"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "unitbox an advanced object detection network"

Connected through hyperedge HE-6 (induced by source paper "real time rotation invariant face detection with progressive calibration networks"):
  with members:
    - "ssh single stage headless face detector"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "r fcn object detection via region based fully convolutional networks"
    - "multi view face detection using deep convolutional neural networks"
    - "spatial transformer networks"

Connected through hyperedge HE-7 (induced by source paper "masquer hunter adversarial occlusion aware face detection"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "face detection through scale friendly deep convolutional networks"
    - "gm net learning features with more efficiency"
    - "recurrent scale approximation for object detection in cnn"
    - "perceptual generative adversarial networks for small object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "face attention network an effective face detector for the occluded faces"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "focal loss for dense object detection"
    - "face detection through scale friendly deep convolutional networks"
    - "scale aware face detection"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "masquer hunter adversarial occlusion aware face 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)