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: "tensormask a foundation for dense object segmentation"
  Abstract: Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available.

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 "pointins point based instance segmentation"):
  with members:
    - "deep snake for real time instance segmentation"
    - "embedmask embedding coupling for one stage instance segmentation"
    - "polarmask single shot instance segmentation with polar representation"
    - "mask scoring r cnn"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "solo segmenting objects by locations"

Connected through hyperedge HE-3 (induced by source paper "blendmask top down meets bottom up for instance segmentation"):
  with members:
    - "pointrend image segmentation as rendering"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "polarmask single shot instance segmentation with polar representation"
    - "yolact better real time instance segmentation"
    - "imp instance mask projection for high accuracy semantic segmentation of things"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "solo segmenting objects by locations"

Connected through hyperedge HE-4 (induced by source paper "patchperpix for instance segmentation"):
  with members:
    - "star convex polyhedra for 3d object detection and segmentation in microscopy"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "affinity derivation and graph merge for instance segmentation"
    - "cell detection with star convex polygons"
    - "semi convolutional operators for instance segmentation"
    - "learned watershed end to end learning of seeded segmentation"
    - "instance sensitive fully convolutional networks"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "pointrend image segmentation as rendering"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "high resolution representations for labeling pixels and regions"
    - "panoptic feature pyramid networks"
    - "efficient segmentation learning downsampling near semantic boundaries"
    - "rethinking imagenet pre training"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "shape aware feature extraction for instance segmentation"):
  with members:
    - "polarmask single shot instance segmentation with polar representation"
    - "mask scoring r cnn"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bottom up object detection by grouping extreme and center points"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"
    - "pixelwise instance segmentation with a dynamically instantiated network"

Connected through hyperedge HE-7 (induced by source paper "dense reppoints representing visual objects with dense point sets"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "reppoints point set representation for object detection"
    - "pointrend image segmentation as rendering"
    - "centernet keypoint triplets for object detection"
    - "polarmask single shot instance segmentation with polar representation"
    - "yolact real time instance segmentation"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"

Connected through hyperedge HE-8 (induced by source paper "centermask real time anchor free instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "centernet keypoint triplets for object detection"
    - "an energy and gpu computation efficient backbone network for real time object detection"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "mask scoring r cnn"
    - "high resolution representations for labeling pixels and regions"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"

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)