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: "mirror mirror on the wall tell me is the error small"
  Abstract: Do object part localization methods produce bilaterally symmetric results on mirror images? Surprisingly not, even though state of the art methods augment the training set with mirrored images. In this paper we take a closer look into this issue. We first introduce the concept of mirrorability as the ability of a model to produce symmetric results in mirrored images and introduce a corresponding measure, namely the \textit{mirror error} that is defined as the difference between the detection result on an image and the mirror of the detection result on its mirror image. We evaluate the mirrorability of several state of the art algorithms in two of the most intensively studied problems, namely human pose estimation and face alignment. Our experiments lead to several interesting findings: 1) Surprisingly, most of state of the art methods struggle to preserve the mirror symmetry, despite the fact that they do have very similar overall performance on the original and mirror images; 2) the low mirrorability is not caused by training or testing sample bias - all algorithms are trained on both the original images and their mirrored versions; 3) the mirror error is strongly correlated to the localization/alignment error (with correlation coefficients around 0.7). Since the mirror error is calculated without knowledge of the ground truth, we show two interesting applications - in the first it is used to guide the selection of difficult samples and in the second to give feedback in a popular Cascaded Pose Regression method for face alignment.

Connected through hyperedge HE-1 (induced by source paper "image retargetability"):
  with members:
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "learning to compose with professional photographs on the web"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning to compare image patches via convolutional neural networks"
    - "multi task cnn model for attribute prediction"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "priming deep neural networks with synthetic faces for enhanced performance"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "inversefacenet deep single shot inverse face rendering from a single image"

Connected through hyperedge HE-4 (induced by source paper "stacked dense u nets with dual transformers for robust face alignment"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "improving landmark localization with semi supervised learning"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "training deep face recognition systems with synthetic data"):
  with members:
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "morphable face models an open framework"
    - "do we really need to collect millions of faces for effective face recognition"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "synthetic data for text localisation in natural images"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "frankenstein learning deep face representations using small data"
    - "deepid3 face recognition with very deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "failing to learn autonomously identifying perception failures for self driving cars"):
  with members:
    - "deepxplore automated whitebox testing of deep learning systems"
    - "accurate single stage detector using recurrent rolling convolution"
    - "how far are we from solving pedestrian detection"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "introspective perception learning to predict failures in vision systems"
    - "ssd single shot multibox detector"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-7 (induced by source paper "differential generative adversarial networks synthesizing non linear facial variations with limited number of training data"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "face aging with conditional generative adversarial networks"
    - "exprgan facial expression editing with controllable expression intensity"
    - "deep generative contrastive networks for facial expression recognition"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-8 (induced by source paper "dense face alignment"):
  with members:
    - "3d face morphable models in the wild"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "holistically nested edge detection"
    - "pose invariant 3d face alignment"
    - "face alignment assisted by head pose estimation"
    - "transferring landmark annotations for cross dataset face alignment"

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)