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: "connnet a long range relation aware pixel connectivity network for salient segmentation"
  Abstract: Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems utilizing complicated multi-step procedures, including refinement networks and complex graphical models. We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair-based connectivity prediction task. Following the intuition that salient objects can be naturally grouped via semantic-aware connectivity between neighboring pixels, we propose a pure Connectivity Net (ConnNet). ConnNet predicts the connectivity probabilities of each pixel with its neighboring pixels by leveraging multi-level cascade contexts embedded in the image and long-range pixel relations. We investigate our approach on two tasks, namely, salient object segmentation and salient instance-level segmentation, and illustrate that consistent improvements can be obtained by modeling these tasks as connectivity instead of binary segmentation tasks for a variety of network architectures. We achieve the state-of-the-art performance, outperforming or being comparable to existing approaches while reducing inference time due to our less complex approach.

Connected through hyperedge HE-1 (induced by source paper "context integrated and feature refined network for lightweight object parsing"):
  with members:
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rtseg real time semantic segmentation comparative study"
    - "image super resolution using very deep residual channel attention networks"
    - "guided upsampling network for real time semantic segmentation"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "cagnet content aware guidance for salient object detection"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "saliency for fine grained object recognition in domains with scarce training data"
    - "learning a discriminative feature network for semantic segmentation"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "probabilistic saliency estimation"
    - "video salient object detection via fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "a cost effective solution for road crack inspection using cameras and deep neural networks"):
  with members:
    - "feature pyramid and hierarchical boosting network for pavement crack detection"
    - "yolov3 an incremental improvement"
    - "road damage detection using deep neural networks with images captured through a smartphone"
    - "image to image translation with conditional adversarial networks"
    - "conditional generative adversarial nets"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "efficient attention attention with linear complexities"):
  with members:
    - "very deep self attention networks for end to end speech recognition"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "learning depth with convolutional spatial propagation network"
    - "yolov3 an incremental improvement"
    - "video based person re identification via 3d convolutional networks and non local attention"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "non local recurrent network for image restoration"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "padnet pan density crowd counting"):
  with members:
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "gaitset regarding gait as a set for cross view gait recognition"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "an aggregated multicolumn dilated convolution network for perspective free counting"
    - "crowd counting by adaptively fusing predictions from an image pyramid"

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)