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: "learning from synthetic data for crowd counting in the wild"
  Abstract: Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of people cause the current methods can not work well. In addition, due to the scarce data, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower. Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations. Extensive experiments show that the first method achieves the state-of-the-art performance on four real datasets, and the second outperforms our baselines. The dataset and source code are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network"):
  with members:
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "few example object detection with model communication"
    - "mvx net multimodal voxelnet for 3d object detection"
    - "dafe fd density aware feature enrichment for face detection"
    - "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"
    - "bam bottleneck attention module"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"
    - "a ccnn adaptive ccnn for density estimation and crowd counting"

Connected through hyperedge HE-4 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "perspective guided convolution networks for crowd counting"

Connected through hyperedge HE-5 (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"
    - "domain adaptive crowd counting via inter domain features segregation and gaussian prior reconstruction"

Connected through hyperedge HE-6 (induced by source paper "encoder decoder based convolutional neural networks with multi scale aware modules for crowd counting"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "dual path multi scale fusion networks with attention for crowd counting"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "learning spatial awareness to improve crowd counting"
    - "crowd counting with deep structured scale integration network"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"

Connected through hyperedge HE-7 (induced by source paper "3d crowd counting via multi view fusion with 3d gaussian kernels"):
  with members:
    - "learnable triangulation of human pose"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "context aware crowd counting"
    - "deepmvs learning multi view stereopsis"
    - "revisiting perspective information for efficient crowd counting"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "crowd counting by adaptively fusing predictions from an image pyramid"

Connected through hyperedge HE-8 (induced by source paper "focus on semantic consistency for cross domain crowd understanding"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "learning to adapt structured output space for semantic segmentation"
    - "adaptive scenario discovery for crowd counting"

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)