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: "part localization using multi proposal consensus for fine grained categorization"
  Abstract: We present a simple deep learning framework to simultaneously predict keypoint locations and their respective visibilities and use those to achieve state-of-the-art performance for fine-grained classification. We show that by conditioning the predictions on object proposals with sufficient image support, our method can do well without complicated spatial reasoning. Instead, inference methods with robustness to outliers, yield state-of-the-art for keypoint localization. We demonstrate the effectiveness of our accurate keypoint localization and visibility prediction on the fine-grained bird recognition task with and without ground truth bird bounding boxes, and outperform existing state-of-the-art methods by over 2%.

Connected through hyperedge HE-1 (induced by source paper "l relu piece wise linear activation functions for deep fine grained visual categorization"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "multi attention multi class constraint for fine grained image recognition"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "self normalizing neural networks"

Connected through hyperedge HE-2 (induced by source paper "improving 3d object detection for pedestrians with virtual multi view synthesis orientation estimation"):
  with members:
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "acquisition of localization confidence for accurate object detection"
    - "soccer on your tabletop"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "in defense of classical image processing fast depth completion on the cpu"
    - "capnet continuous approximation projection for 3d point cloud reconstruction using 2d supervision"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-3 (induced by source paper "3d interpreter networks for viewer centered wireframe modeling"):
  with members:
    - "learning a multi view stereo machine"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "semantic scene completion from a single depth image"
    - "stacked hourglass networks for human pose estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of object landmarks by factorized spatial embeddings"):
  with members:
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "lift learned invariant feature transform"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "learning representations for automatic colorization"
    - "ssd single shot multibox detector"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "fully trainable deep matching"

Connected through hyperedge HE-5 (induced by source paper "mining object parts from cnns via active question answering"):
  with members:
    - "growing interpretable part graphs on convnets via multi shot learning"
    - "webly supervised learning of convolutional networks"
    - "inverting visual representations with convolutional networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "understanding deep features with computer generated imagery"
    - "fast r cnn"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-6 (induced by source paper "interspecies knowledge transfer for facial keypoint detection"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "what makes imagenet good for transfer learning"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "robust face alignment using a mixture of invariant experts"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "single image 3d interpreter network"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "3dmatch learning local geometric descriptors from rgb d reconstructions"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "spatial transformer networks"
    - "deep convolutional inverse graphics network"
    - "a dual source approach for 3d pose estimation from a single image"

Connected through hyperedge HE-8 (induced by source paper "the unreasonable effectiveness of noisy data for fine grained recognition"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "weakly supervised fine grained categorization with part based image representation"
    - "yfcc100m the new data in multimedia research"
    - "a large scale car dataset for fine grained categorization and verification"
    - "webly supervised learning of convolutional networks"
    - "the new data and new challenges in multimedia research"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer 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)