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 decompose for object detection and instance segmentation"
  Abstract: Although deep convolutional neural networks(CNNs) have achieved remarkable results on object detection and segmentation, pre- and post-processing steps such as region proposals and non-maximum suppression(NMS), have been required. These steps result in high computational complexity and sensitivity to hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel end-to-end trainable deep neural network architecture, which consists of convolutional and recurrent layers, that generates the correct number of object instances and their bounding boxes (or segmentation masks) given an image, using only a single network evaluation without any pre- or post-processing steps. We have tested on detecting digits in multi-digit images synthesized using MNIST, automatically segmenting digits in these images, and detecting cars in the KITTI benchmark dataset. The proposed approach outperforms a strong CNN baseline on the synthesized digits datasets and shows promising results on KITTI car detection.

Connected through hyperedge HE-1 (induced by source paper "deep semantic instance segmentation of tree like structures using synthetic data"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pu net point cloud upsampling network"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "domain randomization and generative models for robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "semantic instance segmentation with a discriminative loss function"

Connected through hyperedge HE-2 (induced by source paper "recurrent neural networks for semantic instance segmentation"):
  with members:
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "semantic instance segmentation with a discriminative loss function"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "bottom up instance segmentation using deep higher order crfs"
    - "end to end learning for self driving cars"
    - "deep watershed transform for instance segmentation"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "semantic instance segmentation with a discriminative loss function"):
  with members:
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "gland instance segmentation using deep multichannel neural networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "bottom up instance segmentation using deep higher order crfs"
    - "boundary aware instance segmentation"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "pose2instance harnessing keypoints for person instance segmentation"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "bottom up instance segmentation using deep higher order crfs"
    - "chained predictions using convolutional neural networks"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "a multipath network for object detection"
    - "real time human pose estimation from video with convolutional neural networks"
    - "iterative instance segmentation"
    - "proposal free network for instance level object segmentation"

Connected through hyperedge HE-5 (induced by source paper "end to end instance segmentation with recurrent attention"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "deep watershed transform for instance segmentation"
    - "instancecut from edges to instances with multicut"
    - "learning to refine object segments"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "counting everyday objects in everyday scenes"

Connected through hyperedge HE-6 (induced by source paper "end to end instance segmentation and counting with recurrent attention"):
  with members:
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "counting everyday objects in everyday scenes"
    - "exploring models and data for image question answering"
    - "learning to segment object candidates"
    - "instance aware semantic segmentation via multi task network cascades"
    - "proposal free network for instance level object segmentation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "monocular object instance segmentation and depth ordering with cnns"

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)