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: "end to end instance segmentation and counting with recurrent attention"
  Abstract: While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem; however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. The network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. The proposed model achieves state-of-the-art results on the CVPPP leaf segmentation dataset and KITTI vehicle segmentation dataset.

Connected through hyperedge HE-1 (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"
    - "bottom up instance segmentation using deep higher order crfs"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "semantic segmentation of video sequences with convolutional lstms"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "semantic object parsing with graph lstm"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised instance segmentation using class peak response"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "mask r cnn"
    - "soft proposal networks for weakly supervised object localization"
    - "pixel level encoding and depth layering for instance level semantic labeling"

Connected through hyperedge HE-4 (induced by source paper "attention gated networks for improving ultrasound scan plane detection"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "learn to pay attention"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "non local neural networks"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"
    - "residual attention network for image classification"

Connected through hyperedge HE-5 (induced by source paper "spatial memory for context reasoning in object detection"):
  with members:
    - "an implementation of faster rcnn with study for region sampling"
    - "neural map structured memory for deep reinforcement learning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "cognitive mapping and planning for visual navigation"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "grounding of textual phrases in images by reconstruction"

Connected through hyperedge HE-6 (induced by source paper "recurrent segmentation for variable computational budgets"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "stacked deconvolutional network for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bridging category level and instance level semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "pixelwise instance segmentation with a dynamically instantiated network"):
  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"
    - "joint graph decomposition and node labeling by local search"
    - "boundary aware instance segmentation"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "neural motifs scene graph parsing with global context"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "an implementation of faster rcnn with study for region sampling"
    - "neural amr sequence to sequence models for parsing and generation"
    - "learning to reason end to end module networks for visual question answering"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "obj2text generating visually descriptive language from object layouts"

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.LG(Machine Learning)