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: "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
  Abstract: This paper proposes a crowd counting method. Crowd counting is difficult because of large appearance changes of a target which caused by density and scale changes. Conventional crowd counting methods generally utilize one predictor (e,g., regression and multi-class classifier). However, such only one predictor can not count targets with large appearance changes well. In this paper, we propose to predict the number of targets using multiple CNNs specialized to a specific appearance, and those CNNs are adaptively selected according to the appearance of a test image. By integrating the selected CNNs, the proposed method has the robustness to large appearance changes. In experiments, we confirm that the proposed method can count crowd with lower counting error than a CNN and integration of CNNs with fixed weights. Moreover, we confirm that each predictor automatically specialized to a specific appearance.

Connected through hyperedge HE-1 (induced by source paper "nas count counting by density with neural architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-2 (induced by source paper "attend to count crowd counting with adaptive capacity multi scale cnns"):
  with members:
    - "dual path multi scale fusion networks with attention for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "crowd counting using scale aware attention networks"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "context aware crowd counting"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-3 (induced by source paper "crowd counting with decomposed uncertainty"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "iterative crowd counting"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"

Connected through hyperedge HE-4 (induced by source paper "adaptive weighting multi field of view cnn for semantic segmentation in pathology"):
  with members:
    - "autofocus layer for semantic segmentation"
    - "improving whole slide segmentation through visual context a systematic study"
    - "switching convolutional neural network for crowd counting"
    - "squeeze and excitation networks"
    - "learning convolutional networks for content weighted image compression"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"

Connected through hyperedge HE-5 (induced by source paper "crowd counting and density estimation by trellis encoder decoder network"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "in defense of single column networks for crowd counting"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "model free tracking with deep appearance and motion features integration"
    - "light weight refinenet for real time semantic segmentation"
    - "class agnostic counting"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-6 (induced by source paper "crowd counting using scale aware attention networks"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "visual translation embedding network for visual relation detection"
    - "crowdnet a deep convolutional network for dense crowd counting"
    - "fully convolutional crowd counting on highly congested scenes"
    - "r fcn object detection via region based fully convolutional networks"
    - "attention to scale scale aware semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "coda counting objects via scale aware adversarial density adaption"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "learning to adapt structured output space for semantic segmentation"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "crowd counting with density adaption networks"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (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"
    - "understanding traffic density from large scale web camera data"
    - "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"
    - "ssd single shot multibox detector"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

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)