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: "warpnet weakly supervised matching for single view reconstruction"
  Abstract: We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised single-view reconstruction. This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone. We overcome this challenge through a novel deep learning architecture, WarpNet, that aligns an object in one image with a different object in another. We exploit the structure of the fine-grained dataset to create artificial data for training this network in an unsupervised-discriminative learning approach. The output of the network acts as a spatial prior that allows generalization at test time to match real images across variations in appearance, viewpoint and articulation. On the CUB-200-2011 dataset of bird categories, we improve the AP over an appearance-only network by 13.6%. We further demonstrate that our WarpNet matches, together with the structure of fine-grained datasets, allow single-view reconstructions with quality comparable to using annotated point correspondences.

Connected through hyperedge HE-1 (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-2 (induced by source paper "palmprint recognition in uncontrolled and uncooperative environment"):
  with members:
    - "end to end weakly supervised semantic alignment"
    - "palmprint recognition using deep scattering convolutional network"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "pose invariant 3d face alignment"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-3 (induced by source paper "implicitly defined layers in neural networks"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "optnet differentiable optimization as a layer in neural networks"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "unsupervised learning of 3d structure from images"
    - "input convex neural networks"

Connected through hyperedge HE-4 (induced by source paper "pnp net a hybrid perspective n point network"):
  with members:
    - "learning to find good correspondences"
    - "lift learned invariant feature transform"
    - "fully trainable deep matching"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of pretext invariant representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "s4l self supervised semi supervised learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"

Connected through hyperedge HE-6 (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-7 (induced by source paper "learning semantic correspondence exploiting an object level prior"):
  with members:
    - "sfnet learning object aware semantic correspondence"
    - "hyperpixel flow semantic correspondence with multi layer neural features"
    - "learning correspondence from the cycle consistency of time"
    - "recurrent transformer networks for semantic correspondence"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"

Connected through hyperedge HE-8 (induced by source paper "object landmark discovery through unsupervised adaptation"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "learning correspondence from the cycle consistency of time"
    - "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "simple baselines for human pose estimation and tracking"
    - "unsupervised discovery of object landmarks as structural representations"
    - "ganimation anatomically aware facial animation from a single image"

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)