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: "salient object subitizing"
  Abstract: We study the problem of Salient Object Subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1-4). To this end, we present a salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained Convolutional Neural Network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.

Connected through hyperedge HE-1 (induced by source paper "towards partial supervision for generic object counting in natural scenes"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "learning spatial awareness to improve crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "where are the masks instance segmentation with image level supervision"
    - "object counting and instance segmentation with image level supervision"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-2 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "deepqoe a unified framework for learning to predict video qoe"
    - "instance level salient object segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "quasi homography warps in image stitching"
    - "instance sensitive fully convolutional networks"
    - "convolutional oriented boundaries"
    - "learning to refine object segments"

Connected through hyperedge HE-3 (induced by source paper "salient instance segmentation via subitizing and clustering"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "structure measure a new way to evaluate foreground maps"
    - "squeeze and excitation networks"
    - "instance level salient object segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "object counting and instance segmentation with image level supervision"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "weakly supervised instance segmentation using class peak response"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "c wsl count guided weakly supervised localization"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "soft proposal networks for weakly supervised object localization"

Connected through hyperedge HE-5 (induced by source paper "simco similarity based object counting"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "class agnostic counting"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "in defense of the triplet loss for person re identification"
    - "how intelligent are convolutional neural networks"
    - "beyond counting comparisons of density maps for crowd analysis tasks counting detection and tracking"
    - "representation learning by learning to count"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"

Connected through hyperedge HE-6 (induced by source paper "a weakly supervised approach for estimating spatial density functions from high resolution satellite imagery"):
  with members:
    - "on the convergence of adam and beyond"
    - "weakly supervised instance segmentation using class peak response"
    - "uncertainty gated network for land cover segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "learning transferable architectures for scalable image recognition"
    - "c wsl count guided weakly supervised localization"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-7 (induced by source paper "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "towards the success rate of one real time unconstrained salient object detection"
    - "label refinement network for coarse to fine semantic segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-8 (induced by source paper "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"):
  with members:
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "an overview of multi task learning in deep neural networks"
    - "cross stitch networks for multi task learning"
    - "learning without forgetting"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "counting everyday objects in everyday scenes"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

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)