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: "understanding traffic density from large scale web camera data"
  Abstract: Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective. To deeply understand traffic density, we explore both deep learning based and optimization based methods. To avoid individual vehicle detection and tracking, both methods map the image into vehicle density map, one based on rank constrained regression and the other one based on fully convolution networks (FCN). The regression based method learns different weights for different blocks in the image to increase freedom degrees of weights and embed perspective information. The FCN based method jointly estimates vehicle density map and vehicle count with a residual learning framework to perform end-to-end dense prediction, allowing arbitrary image resolution, and adapting to different vehicle scales and perspectives. We analyze and compare both methods, and get insights from optimization based method to improve deep model. Since existing datasets do not cover all the challenges in our work, we collected and labelled a large-scale traffic video dataset, containing 60 million frames from 212 webcams. Both methods are extensively evaluated and compared on different counting tasks and datasets. FCN based method significantly reduces the mean absolute error from 10.99 to 5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.

Connected through hyperedge HE-1 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey"):
  with members:
    - "multi source domain adaptation for text classification via distancenet bandits"
    - "multi source domain adaptation for visual sentiment classification"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "multi source distilling domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "from data to actions in intelligent transportation systems a prescription of functional requirements for model actionability"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "predicting transportation modes of gps trajectories using feature engineering and noise removal"
    - "explanation methods in deep learning users values concerns and challenges"
    - "transferable pedestrian motion prediction models at intersections"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "when traffic flow prediction meets wireless big data analytics"

Connected through hyperedge HE-3 (induced by source paper "counting with focus for free"):
  with members:
    - "crowd counting using scale aware attention networks"
    - "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"
    - "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"
    - "iterative crowd counting"

Connected through hyperedge HE-4 (induced by source paper "towards locally consistent object counting with constrained multi stage convolutional neural networks"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "stacked hourglass networks for human pose estimation"
    - "iterative instance segmentation"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-5 (induced by source paper "pose guided attention for multi label fashion image classification"):
  with members:
    - "learning what and where to attend"
    - "cbam convolutional block attention module"
    - "focal loss for dense object detection"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "providentia a large scale sensing system for the assistance of autonomous vehicles"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "targetless rotational auto calibration of radar and camera for intelligent transportation systems"
    - "yolov3 an incremental improvement"
    - "traffic danger recognition with surveillance cameras without training data"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"

Connected through hyperedge HE-7 (induced by source paper "where are the blobs counting by localization with point supervision"):
  with members:
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "count ception counting by fully convolutional redundant counting"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "ssd single shot multibox detector"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "generating high quality crowd density maps using contextual pyramid cnns"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "spatiotemporal modeling for crowd counting in videos"
    - "multi style generative network for real time transfer"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification"
    - "beyond counting comparisons of density maps for crowd analysis tasks counting detection and tracking"

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)