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: "bottom up instance segmentation using deep higher order crfs"
  Abstract: Traditional Scene Understanding problems such as Object Detection and Semantic Segmentation have made breakthroughs in recent years due to the adoption of deep learning. However, the former task is not able to localise objects at a pixel level, and the latter task has no notion of different instances of objects of the same class. We focus on the task of Instance Segmentation which recognises and localises objects down to a pixel level. Our model is based on a deep neural network trained for semantic segmentation. This network incorporates a Conditional Random Field with end-to-end trainable higher order potentials based on object detector outputs. This allows us to reason about instances from an initial, category-level semantic segmentation. Our simple method effectively leverages the great progress recently made in semantic segmentation and object detection. The accurate instance-level segmentations that our network produces is reflected by the considerable improvements obtained over previous work.

Connected through hyperedge HE-1 (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"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "gated scnn gated shape cnns for semantic segmentation"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "adaptive affinity fields for semantic segmentation"
    - "free form image inpainting with gated convolution"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tree structured kronecker convolutional network for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "spatial sampling network for fast scene understanding"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "benchmark analysis of representative deep neural network architectures"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

Connected through hyperedge HE-4 (induced by source paper "hybrid task cascade for instance segmentation"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "meta r cnn towards general solver for instance level few shot learning"):
  with members:
    - "provable guarantees for gradient based meta learning"
    - "panoptic feature pyramid networks"
    - "few shot object detection via feature reweighting"
    - "low shot learning from imaginary data"
    - "lstd a low shot transfer detector for object detection"
    - "dynamic few shot visual learning without forgetting"
    - "detecting and recognizing human object interactions"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "straight to shapes real time instance segmentation made more accurate"):
  with members:
    - "vision meets drones a challenge"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "boundary aware instance segmentation"
    - "yolo9000 better faster stronger"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "actor critic instance segmentation"):
  with members:
    - "lstm a search space odyssey"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "spatial memory for context reasoning in object detection"
    - "semantic instance segmentation with a discriminative loss function"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "end to end instance segmentation and counting with recurrent attention"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-8 (induced by source paper "weakly and semi supervised panoptic segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "path aggregation network for instance segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "holistic instance level human parsing"
    - "extreme clicking for efficient object annotation"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "learning to segment every thing"

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)