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: "adversarial scene editing automatic object removal from weak supervision"
  Abstract: While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that co-operate to remove objects, and a novel GAN based prior for the mask generator that allows us to flexibly incorporate knowledge about object shapes. We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only

Connected through hyperedge HE-1 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation"):
  with members:
    - "modularized textual grounding for counterfactual resilience"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "taskonomy disentangling task transfer learning"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"

Connected through hyperedge HE-2 (induced by source paper "preserving semantic and temporal consistency for unpaired video to video translation"):
  with members:
    - "inserting videos into videos"
    - "deep video inpainting"
    - "deep blind video decaptioning by temporal aggregation and recurrence"
    - "frame recurrent video super resolution"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"

Connected through hyperedge HE-3 (induced by source paper "end to end learning of geometric deformations of feature maps for virtual try on"):
  with members:
    - "towards multi pose guided virtual try on network"
    - "unsupervised part based disentangling of object shape and appearance"
    - "synthesizing images of humans in unseen poses"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "the relativistic discriminator a key element missing from standard gan"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "toward characteristic preserving image based virtual try on network"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-4 (induced by source paper "monet unsupervised scene decomposition and representation"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "path aggregation network for instance segmentation"
    - "tracking emerges by colorizing videos"
    - "metacontrol for adaptive imagination based optimization"

Connected through hyperedge HE-5 (induced by source paper "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"):
  with members:
    - "sunny and dark outside improving answer consistency in vqa through entailed question generation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "cycle consistency for robust visual question answering"
    - "explicit bias discovery in visual question answering models"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "visual question answering datasets algorithms and future challenges"
    - "show ask attend and answer a strong baseline for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "deepusps deep robust unsupervised saliency prediction with self supervision"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "fusionnet and augmentedflownet selective proxy ground truth for training on unlabeled images"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "storygan a sequential conditional gan for story visualization"):
  with members:
    - "chatpainter improving text to image generation using dialogue"
    - "imagine this scripts to compositions to videos"
    - "learning to forecast and refine residual motion for image to video generation"
    - "every smile is unique landmark guided diverse smile generation"
    - "deep video generation prediction and completion of human action sequences"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "tell draw and repeat generating and modifying images based on continual linguistic instruction"
    - "probabilistic video generation using holistic attribute control"

Connected through hyperedge HE-8 (induced by source paper "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"):
  with members:
    - "modeling visual context is key to augmenting object detection datasets"
    - "unified perceptual parsing for scene understanding"
    - "the elephant in the room"
    - "rise randomized input sampling for explanation of black box models"
    - "context encoding for semantic segmentation"
    - "network dissection quantifying interpretability of deep visual representations"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "why should i trust you explaining the predictions of any classifier"

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)