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: "deep learning representation using autoencoder for 3d shape retrieval"
  Abstract: We study the problem of how to build a deep learning representation for 3D shape. Deep learning has shown to be very effective in variety of visual applications, such as image classification and object detection. However, it has not been successfully applied to 3D shape recognition. This is because 3D shape has complex structure in 3D space and there are limited number of 3D shapes for feature learning. To address these problems, we project 3D shapes into 2D space and use autoencoder for feature learning on the 2D images. High accuracy 3D shape retrieval performance is obtained by aggregating the features learned on 2D images. In addition, we show the proposed deep learning feature is complementary to conventional local image descriptors. By combing the global deep learning representation and the local descriptor representation, our method can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.

Connected through hyperedge HE-1 (induced by source paper "an alarm system for segmentation algorithm based on shape model"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (induced by source paper "recurrent neural network based semantic variational autoencoder for sequence to sequence learning"):
  with members:
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "audeep unsupervised learning of representations from audio with deep recurrent neural networks"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "machine comprehension by text to text neural question generation"
    - "variational autoencoders for semi supervised text classification"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-3 (induced by source paper "learning a disentangled embedding for monocular 3d shape retrieval and pose estimation"):
  with members:
    - "monocular object orientation estimation using riemannian regression and classification networks"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "on the continuity of rotation representations in neural networks"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "a unified framework for multi view multi class object pose estimation"

Connected through hyperedge HE-4 (induced by source paper "3d pose estimation and 3d model retrieval for objects in the wild"):
  with members:
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "6 dof object pose from semantic keypoints"
    - "3d bounding box estimation using deep learning and geometry"
    - "learning a predictable and generative vector representation for objects"
    - "identity mappings in deep residual networks"
    - "crafting a multi task cnn for viewpoint estimation"
    - "pose induction for novel object categories"
    - "3d object class detection in the wild"

Connected through hyperedge HE-5 (induced by source paper "visual imitation learning with recurrent siamese networks"):
  with members:
    - "deepmind control suite"
    - "learning actionable representations from visual observations"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "behavioral cloning from observation"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-6 (induced by source paper "learning discriminative 3d shape representations by view discerning networks"):
  with members:
    - "deep learning for logo recognition"
    - "quality aware network for set to set recognition"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "fpnn field probing neural networks for 3d data"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "accurate image super resolution using very deep convolutional networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (induced by source paper "a convolutional autoencoder approach to learn volumetric shape representations for brain structures"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "gift a real time and scalable 3d shape search engine"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-8 (induced by source paper "set cross entropy likelihood based permutation invariant loss function for probability distributions"):
  with members:
    - "learning plannable representations with causal infogan"
    - "deep sets"
    - "end to end differentiable proving"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "shapenet an information rich 3d model repository"

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)