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: "st gan spatial transformer generative adversarial networks for image compositing"
  Abstract: We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image. To achieve this, we propose a novel Generative Adversarial Network (GAN) architecture that utilizes Spatial Transformer Networks (STNs) as the generator, which we call Spatial Transformer GANs (ST-GANs). ST-GANs seek image realism by operating in the geometric warp parameter space. In particular, we exploit an iterative STN warping scheme and propose a sequential training strategy that achieves better results compared to naive training of a single generator. One of the key advantages of ST-GAN is its applicability to high-resolution images indirectly since the predicted warp parameters are transferable between reference frames. We demonstrate our approach in two applications: (1) visualizing how indoor furniture (e.g. from product images) might be perceived in a room, (2) hallucinating how accessories like glasses would look when matched with real portraits.

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"

Connected through hyperedge HE-2 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "fast scnn fast semantic segmentation network"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "referring relationships"
    - "context aware synthesis and placement of object instances"
    - "mattnet modular attention network for referring expression comprehension"
    - "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-3 (induced by source paper "generating object stamps"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "adaptive composition gan towards realistic image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "multistage model for robust face alignment using deep neural networks"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "face alignment using a 3d deeply initialized ensemble of regression trees"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "style aggregated network for facial landmark detection"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "joint multi view face alignment in the wild"

Connected through hyperedge HE-5 (induced by source paper "ga dan geometry aware domain adaptation network for scene text detection and recognition"):
  with members:
    - "adaptive composition gan towards realistic image synthesis"
    - "scene text synthesis for efficient and effective deep network training"
    - "rotation sensitive regression for oriented scene text detection"
    - "fots fast oriented text spotting with a unified network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "accurate scene text detection through border semantics awareness and bootstrapping"

Connected through hyperedge HE-6 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-7 (induced by source paper "adaptive composition gan towards realistic image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spatial fusion gan for image synthesis"

Connected through hyperedge HE-8 (induced by source paper "learning to generate synthetic data via compositing"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "spatially transformed adversarial examples"
    - "loss rank mining a general hard example mining method for real time detectors"
    - "modeling visual context is key to augmenting object detection datasets"
    - "unsupervised hard example mining from videos for improved object detection"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "can 3d pose be learned from 2d projections alone"
    - "autoaugment learning augmentation policies from data"

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)