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: "a fully convolutional two stream fusion network for interactive image segmentation"
  Abstract: In this paper, we propose a novel fully convolutional two-stream fusion network (FCTSFN) for interactive image segmentation. The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution. The TSLFN includes two distinct deep streams followed by a fusion network. The intuition is that, since user interactions are more direct information on foreground/background than the image itself, the two-stream structure of the TSLFN reduces the number of layers between the pure user interaction features and the network output, allowing the user interactions to have a more direct impact on the segmentation result. The MSRN fuses the features from different layers of TSLFN with different scales, in order to seek the local to global information on the foreground to refine the segmentation result at full resolution. We conduct comprehensive experiments on four benchmark datasets. The results show that the proposed network achieves competitive performance compared to current state-of-the-art interactive image segmentation methods

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"
    - "group normalization"
    - "interactive video object segmentation in the wild"
    - "iteratively trained interactive segmentation"
    - "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 "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "video object segmentation with re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-3 (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"
    - "interactive video object segmentation in the wild"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "iteratively trained interactive segmentation"

Connected through hyperedge HE-4 (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"
    - "iteratively trained interactive 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-5 (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"
    - "iteratively trained interactive 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-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:
    - "iteratively trained interactive 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"
    - "iteratively trained interactive 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"

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)