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: "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
  Abstract: Different to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time. The network architecture incorporates convolutional gated recurrent units (ConvGRU) into a stacked hourglass network to utilize temporal video information. Furthermore, we train the network with a novel embedding loss based on cosine similarities, such that the network predicts unique embeddings for every instance throughout videos. Afterwards, these embeddings are clustered among subsequent video frames to create the final tracked instance segmentations. We evaluate the recurrent hourglass network by segmenting left ventricles in MR videos of the heart, where it outperforms a network that does not incorporate video information. Furthermore, we show applicability of the cosine embedding loss for segmenting leaf instances on still images of plants. Finally, we evaluate the framework for instance segmentation and tracking on six datasets of the ISBI celltracking challenge, where it shows state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "data augmentation for leaf segmentation and counting tasks in rosette plants"
    - "mask scoring r cnn"
    - "instance segmentation of biological images using harmonic embeddings"
    - "panoptic feature pyramid networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep leaf segmentation using synthetic data"

Connected through hyperedge HE-2 (induced by source paper "mpm joint representation of motion and position map for cell tracking"):
  with members:
    - "joint multi frame detection and segmentation for multi cell tracking"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "end to end representation learning for correlation filter based tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "poi multiple object tracking with high performance detection and appearance feature"
    - "r cnns for pose estimation and action detection"

Connected through hyperedge HE-3 (induced by source paper "object parsing in sequences using coordconv gated recurrent networks"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "deep supervision with shape concepts for occlusion aware 3d object parsing"

Connected through hyperedge HE-4 (induced by source paper "learning dense voxel embeddings for 3d neuron reconstruction"):
  with members:
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "on the convergence of adam and beyond"
    - "learning metric graphs for neuron segmentation in electron microscopy images"
    - "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-5 (induced by source paper "object guided instance segmentation for biological images"):
  with members:
    - "multi scale cell instance segmentation with keypoint graph based bounding boxes"
    - "hybrid task cascade for instance segmentation"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "locating objects without bounding boxes"
    - "cell detection with star convex polygons"

Connected through hyperedge HE-6 (induced by source paper "joint multi frame detection and segmentation for multi cell tracking"):
  with members:
    - "unet a nested u net architecture for medical image segmentation"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "microscopy cell segmentation via adversarial neural networks"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "stacked hourglass networks for human pose estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deeper into convolutional networks for learning video representations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "multi scale cell instance segmentation with keypoint graph based bounding boxes"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "cell detection with star convex polygons"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "fully convolutional instance aware semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "microscopy cell segmentation via convolutional lstm networks"):
  with members:
    - "microscopy cell segmentation via adversarial neural networks"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "show attend and tell neural image caption generation with visual attention"

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)