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: "iteratively trained interactive segmentation"
  Abstract: Deep learning requires large amounts of training data to be effective. For the task of object segmentation, manually labeling data is very expensive, and hence interactive methods are needed. Following recent approaches, we develop an interactive object segmentation system which uses user input in the form of clicks as the input to a convolutional network. While previous methods use heuristic click sampling strategies to emulate user clicks during training, we propose a new iterative training strategy. During training, we iteratively add clicks based on the errors of the currently predicted segmentation. We show that our iterative training strategy together with additional improvements to the network architecture results in improved results over the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "getting to 99 accuracy in interactive segmentation"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "weight standardization"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "group normalization"
    - "interactive video object segmentation in the wild"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "iterative interaction training for segmentation editing networks"

Connected through hyperedge HE-2 (induced by source paper "continuous adaptation for interactive object segmentation by learning from corrections"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "task free continual learning"
    - "interactive full image segmentation by considering all regions jointly"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "interactive video object segmentation in the wild"
    - "human centric indoor scene synthesis using stochastic grammar"

Connected through hyperedge HE-3 (induced by source paper "panoptic image annotation with a collaborative assistant"):
  with members:
    - "panoptic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "guide me interacting with deep networks"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "blazingly fast video object segmentation with pixel wise metric learning"

Connected through hyperedge HE-4 (induced by source paper "large scale interactive object segmentation with human annotators"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "weakly supervised instance segmentation using class peak response"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep grabcut for object selection"

Connected through hyperedge HE-5 (induced by source paper "freelabel a publicly available annotation tool based on freehand traces"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "semantic segmentation refinement by monte carlo region growing of high confidence detections"
    - "multispecies fruit flower detection using a refined semantic segmentation network"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "apple flower detection using deep convolutional networks"
    - "looking at outfit to parse clothing"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "class independent sequential full image segmentation using a convolutional net that finds a segment within an attention region given a pointer pixel within this segment"):
  with members:
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "interactive full image segmentation by considering all regions jointly"):
  with members:
    - "panoptic segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "guide me interacting with deep networks"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-8 (induced by source paper "a fully convolutional two stream fusion network for interactive image segmentation"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "deep interactive region segmentation and captioning"
    - "deep extreme cut from extreme points to object segmentation"
    - "deep grabcut for object selection"
    - "fast image processing with fully convolutional networks"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

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)