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: "fully convolutional neural networks for crowd segmentation"
  Abstract: In this paper, we propose a fast fully convolutional neural network (FCNN) for crowd segmentation. By replacing the fully connected layers in CNN with 1 by 1 convolution kernels, FCNN takes whole images as inputs and directly outputs segmentation maps by one pass of forward propagation. It has the property of translation invariance like patch-by-patch scanning but with much lower computation cost. Once FCNN is learned, it can process input images of any sizes without warping them to a standard size. These attractive properties make it extendable to other general image segmentation problems. Based on FCNN, a multi-stage deep learning is proposed to integrate appearance and motion cues for crowd segmentation. Both appearance filters and motion filers are pretrained stage-by-stage and then jointly optimized. Different combination methods are investigated. The effectiveness of our approach and component-wise analysis are evaluated on two crowd segmentation datasets created by us, which include image frames from 235 and 11 scenes, respectively. They are currently the largest crowd segmentation datasets and will be released to the public.

Connected through hyperedge HE-1 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting"):
  with members:
    - "multi scale attention network for crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "denet a universal network for counting crowd with varying densities and scales"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "crowd counting using deep recurrent spatial aware network"
    - "context aware crowd counting"
    - "adaptive scenario discovery for crowd counting"

Connected through hyperedge HE-2 (induced by source paper "towards using count level weak supervision for crowd counting"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "dual path multi scale fusion networks with attention for crowd counting"
    - "enhanced 3d convolutional networks for crowd counting"
    - "feature aware adaptation and structured density alignment for crowd counting in video surveillance"
    - "w net reinforced u net for density map estimation"
    - "dynamic region division for adaptive learning pedestrian counting"
    - "learning from synthetic data for crowd counting in the wild"

Connected through hyperedge HE-3 (induced by source paper "domain adaptive crowd counting via inter domain features segregation and gaussian prior reconstruction"):
  with members:
    - "c 3 framework an open source pytorch code for crowd counting"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
    - "learning from synthetic data for crowd counting in the wild"
    - "crowd counting with deep structured scale integration network"
    - "generalizing semi supervised generative adversarial networks to regression using feature contrasting"

Connected through hyperedge HE-4 (induced by source paper "a miniaturized semantic segmentation method for remote sensing image"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "understanding convolution for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-5 (induced by source paper "hybrid cnn and dictionary based models for scene recognition and domain adaptation"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "learning transferable features with deep adaptation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "places205 vggnet models for scene recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "a survey of recent advances in cnn based single image crowd counting and density estimation"):
  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"
    - "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"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "image de raining using a conditional generative adversarial network"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "object detection in videos with tubelet proposal networks"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "person search with natural language description"
    - "object detection from video tubelets with convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "seq nms for video object detection"
    - "learning to track at 100 fps with deep regression networks"
    - "joint detection and identification feature learning for person search"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"):
  with members:
    - "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "crowdnet a deep convolutional network for dense crowd counting"
    - "fully convolutional crowd counting on highly congested scenes"
    - "instance aware semantic segmentation via multi task network cascades"
    - "crowded scene analysis a survey"
    - "people counting in high density crowds from still images"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

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)