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: "counting everyday objects in everyday scenes"
  Abstract: We are interested in counting the number of instances of object classes in natural, everyday images. Previous counting approaches tackle the problem in restricted domains such as counting pedestrians in surveillance videos. Counts can also be estimated from outputs of other vision tasks like object detection. In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes. Our approach is inspired by the phenomenon of subitizing - the ability of humans to make quick assessments of counts given a perceptual signal, for small count values. Given a natural scene, we employ a divide and conquer strategy while incorporating context across the scene to adapt the subitizing idea to counting. Our approach offers consistent improvements over numerous baseline approaches for counting on the PASCAL VOC 2007 and COCO datasets. Subsequently, we study how counting can be used to improve object detection. We then show a proof of concept application of our counting methods to the task of Visual Question Answering, by studying the `how many?' questions in the VQA and COCO-QA datasets.

Connected through hyperedge HE-1 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "learning spatial awareness to improve crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "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"
    - "crowd counting using deep recurrent spatial aware network"

Connected through hyperedge HE-2 (induced by source paper "rethinking object detection in retail stores"):
  with members:
    - "reppoints point set representation for object detection"
    - "data priming network for automatic check out"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "rpc a large scale retail product checkout dataset"
    - "object counting and instance segmentation with image level supervision"
    - "cornernet detecting objects as paired keypoints"
    - "mvtec d2s densely segmented supermarket dataset"

Connected through hyperedge HE-3 (induced by source paper "scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "icdar 2019 competition on scene text visual question answering"
    - "single shot scene text retrieval"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "tallyqa answering complex counting questions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-4 (induced by source paper "baod budget aware object detection"):
  with members:
    - "a flexible model for training action localization with varying levels of supervision"
    - "pcn part and context information for pedestrian detection with cnns"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "localization aware active learning for object detection"
    - "towards practical visual search engine within elasticsearch"
    - "generative adversarial active learning"
    - "learning active learning from data"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "adversarial regularization for visual question answering strengths shortcomings and side effects"):
  with members:
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "learning to count objects in natural images for visual question answering"
    - "hypothesis only baselines in natural language inference"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "overcoming language priors in visual question answering with adversarial regularization"

Connected through hyperedge HE-7 (induced by source paper "bayesian loss for crowd count estimation with point supervision"):
  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"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "iterative crowd counting"
    - "an aggregated multicolumn dilated convolution network for perspective free counting"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-8 (induced by source paper "aggregation cross entropy for sequence recognition"):
  with members:
    - "edit probability for scene text recognition"
    - "attention is all you need"
    - "scene text recognition with sliding convolutional character models"
    - "focusing attention towards accurate text recognition in natural images"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "hierarchical question image co attention for visual question answering"

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)