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: "unsupervised learning of object landmarks by factorized spatial embeddings"
  Abstract: Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep neural network that detects landmarks consistently with such visual effects. Furthermore, we show that the learned landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly. We assess the method qualitatively on a variety of object types, natural and man-made. We also show that our unsupervised landmarks are highly predictive of manually-annotated landmarks in face benchmark datasets, and can be used to regress these with a high degree of accuracy.

Connected through hyperedge HE-1 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency"):
  with members:
    - "photo geometric autoencoding to learn 3d objects from unlabelled images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "scops self supervised co part segmentation"
    - "learning to infer implicit surfaces without 3d supervision"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning view priors for single view 3d reconstruction"

Connected through hyperedge HE-2 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised part based disentangling of object shape and appearance"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised learning of landmarks by descriptor vector exchange"

Connected through hyperedge HE-3 (induced by source paper "unsupervised disentanglement of pose appearance and background from images and videos"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "neural scene decomposition for multi person motion capture"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video interpolation and prediction with unsupervised landmarks"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-4 (induced by source paper "learning landmarks from unaligned data using image translation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to read by spelling towards unsupervised text recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "self supervised learning of a facial attribute embedding from video"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "3d human pose estimation in the wild by adversarial learning"
    - "unsupervised discovery of object landmarks as structural representations"

Connected through hyperedge HE-5 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep interpretable non rigid structure from motion"
    - "face alignment in full pose range a 3d total solution"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "3d aware scene manipulation via inverse graphics"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-6 (induced by source paper "transgaga geometry aware unsupervised image to image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "every smile is unique landmark guided diverse smile generation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "unsupervised discovery of object landmarks as structural representations"

Connected through hyperedge HE-7 (induced by source paper "self supervised learning of class embeddings from video"):
  with members:
    - "learning landmarks from unaligned data using image translation"
    - "growing a brain fine tuning by increasing model capacity"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "self supervised learning of a facial attribute embedding from video"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "unsupervised discovery of object landmarks as structural representations"
    - "a fully progressive approach to single image super resolution"
    - "voxceleb2 deep speaker recognition"

Connected through hyperedge HE-8 (induced by source paper "unsupervised keypoint learning for guiding class conditional video prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"
    - "unsupervised discovery of object landmarks as structural representations"
    - "stochastic video generation with a learned prior"

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)