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: "robust lstm autoencoders for face de occlusion in the wild"
  Abstract: Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers which is heavily desired in real-world applications concerning surveillance and security. Although much research effort has been devoted to developing face de-occlusion methods, most of them can only work well under constrained conditions, such as all the faces are from a pre-defined closed set. In this paper, we propose a robust LSTM-Autoencoders (RLA) model to effectively restore partially occluded faces even in the wild. The RLA model consists of two LSTM components, which aims at occlusion-robust face encoding and recurrent occlusion removal respectively. The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches. Receiving the representation learned by the encoder, the LSTM decoder with a dual channel architecture reconstructs the overall face and detects occlusion simultaneously, and by feat of LSTM, the decoder breaks down the task of face de-occlusion into restoring the occluded part step by step. Moreover, to minimize identify information loss and guarantee face recognition accuracy over recovered faces, we introduce an identity-preserving adversarial training scheme to further improve RLA. Extensive experiments on both synthetic and real datasets of faces with occlusion clearly demonstrate the effectiveness of our proposed RLA in removing different types of facial occlusion at various locations. The proposed method also provides significantly larger performance gain than other de-occlusion methods in promoting recognition performance over partially-occluded faces.

Connected through hyperedge HE-1 (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-2 (induced by source paper "road segmentation with image lidar data fusion"):
  with members:
    - "dynamically visual disambiguation of keyword based image search"
    - "extracting visual knowledge from the internet making sense of image data"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "exploiting web images for dataset construction a domain robust approach"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "face de occlusion using 3d morphable model and generative adversarial network"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "geometry aware face completion and editing"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "3d face reconstruction with geometry details from a single image"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-4 (induced by source paper "autonomous skill discovery with quality diversity and unsupervised descriptors"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "real world evolution adapts robot morphology and control to hardware limitations"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "hierarchical behavioral repertoires with unsupervised descriptors"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"

Connected through hyperedge HE-5 (induced by source paper "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "production level facial performance capture using deep convolutional neural networks"
    - "deep cascaded bi network for face hallucination"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"

Connected through hyperedge HE-6 (induced by source paper "inversefacenet deep single shot inverse face rendering from a single image"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "3d face morphable models in the wild"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "failure detection for facial landmark detectors"

Connected through hyperedge HE-7 (induced by source paper "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "triplet probabilistic embedding for face verification and clustering"
    - "triplet similarity embedding for face verification"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

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)