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: "recurrent neural networks for semantic instance segmentation"
  Abstract: We present a recurrent model for semantic instance segmentation that sequentially generates pairs of masks and their associated class probabilities for every object in an image. Our proposed system is trainable end-to-end, does not require post-processing steps on its output and is conceptually simpler than current methods relying on object proposals. We observe that our model learns to follow a consistent pattern to generate object sequences, which correlates with the activations learned in the encoder part of our network. We achieve competitive results on three different instance segmentation benchmarks (Pascal VOC 2012, Cityscapes and CVPPP Plant Leaf Segmentation). Code is available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "data augmentation for leaf segmentation and counting tasks in rosette plants"
    - "mask scoring r cnn"
    - "instance segmentation of biological images using harmonic embeddings"
    - "panoptic feature pyramid networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep leaf segmentation using synthetic data"

Connected through hyperedge HE-2 (induced by source paper "budget aware semi supervised semantic and instance segmentation"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning to segment via cut and paste"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "pseudo mask augmented object detection"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "rvos end to end recurrent network for video object 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"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "youtube vos sequence to sequence video object segmentation"
    - "youtube vos a large scale video object segmentation benchmark"

Connected through hyperedge HE-4 (induced by source paper "unifying part detection and association for recurrent multi person pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
    - "towards accurate multi person pose estimation in the wild"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "posetrack joint multi person pose estimation and tracking"

Connected through hyperedge HE-5 (induced by source paper "recurrent instance segmentation using sequences of referring expressions"):
  with members:
    - "cross modal self attention network for referring image segmentation"
    - "mattnet modular attention network for referring expression comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mask r cnn"
    - "segmentation from natural language expressions"
    - "modeling context in referring expressions"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)