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 and low rank representation for fast face identification with occlusions"
  Abstract: In this paper, we propose an iterative method to address the face identification problem with block occlusions. Our approach utilizes a robust representation based on two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. The first fits to the errors a distribution described by a tailored loss function. The second describes the error image as having a specific structure (resulting in low-rank in comparison with image size). We will show that this joint characterization is effective for describing errors with spatial continuity. Our approach is computationally efficient due to the utilization of the alternating direction method of multipliers. A special case of our fast iterative algorithm leads to the robust representation method, which is normally used to handle non-contiguous errors (e.g., pixel corruption). Extensive results on representative face databases (in constrained and unconstrained environments) document the effectiveness of our method over existing robust representation methods with respect to both identification rates and computational time.

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 "robust lstm autoencoders for face de occlusion in the wild"):
  with members:
    - "multi view intact space learning"
    - "context encoders feature learning by inpainting"
    - "unsupervised learning of video representations using lstms"
    - "show attend and tell neural image caption generation with visual attention"
    - "facenet a unified embedding for face recognition and clustering"
    - "draw a recurrent neural network for image generation"
    - "going deeper with convolutions"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-3 (induced by source paper "object classification with joint projection and low rank dictionary learning"):
  with members:
    - "on the invariance of dictionary learning and sparse representation to projecting data to a discriminative space"
    - "jointly learning non negative projection and dictionary with discriminative graph constraints for classification"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "pcanet a simple deep learning baseline for image classification"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "robust recovery of subspace structures by low rank representation"

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)