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: "learning to refine object segments"
  Abstract: Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture rich spatial information, while upper layers encode object-level knowledge but are invariant to factors such as pose and appearance. In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach. The resulting bottom-up/top-down architecture is capable of efficiently generating high-fidelity object masks. Similarly to skip connections, our approach leverages features at all layers of the net. Unlike skip connections, our approach does not attempt to output independent predictions at each layer. Instead, we first output a coarse `mask encoding' in a feedforward pass, then refine this mask encoding in a top-down pass utilizing features at successively lower layers. The approach is simple, fast, and effective. Building on the recent DeepMask network for generating object proposals, we show accuracy improvements of 10-20% in average recall for various setups. Additionally, by optimizing the overall network architecture, our approach, which we call SharpMask, is 50% faster than the original DeepMask network (under .8s per image).

Connected through hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep portrait image completion and extrapolation"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "generative face completion"
    - "a generative model of people in clothing"

Connected through hyperedge HE-3 (induced by source paper "joint learning of instance and semantic segmentation for robotic pick and place with heavy occlusions in clutter"):
  with members:
    - "probabilistic 3d multilabel real time mapping for multi object manipulation"
    - "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "joint learning of intrinsic images and semantic segmentation"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "segflow joint learning for video object segmentation and optical flow"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"):
  with members:
    - "probabilistic 3d multilabel real time mapping for multi object manipulation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "on pre trained image features and synthetic images for deep learning"
    - "synthesizing training data for object detection in indoor scenes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning from simulated and unsupervised images through adversarial training"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

Connected through hyperedge HE-5 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation"):
  with members:
    - "track to reconstruct and reconstruct to track"
    - "stereo based multi motion visual odometry for mobile robots"
    - "unsupervised monocular depth and ego motion learning with structure and semantics"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"

Connected through hyperedge HE-6 (induced by source paper "flowfusion dynamic dense rgb d slam based on optical flow"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "optical flow in mostly rigid scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-7 (induced by source paper "deeperlab single shot image parser"):
  with members:
    - "panoptic segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "affinity derivation and graph merge for instance segmentation"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "animal detection in man made environments"):
  with members:
    - "looking fast and slow memory guided mobile video object detection"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "the iwildcam 2019 challenge dataset"
    - "searching for mobilenetv3"
    - "the via annotation software for images audio and video"
    - "large scale interactive object segmentation with human annotators"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

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)