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 spatial awareness to improve crowd counting"
  Abstract: The aim of crowd counting is to estimate the number of people in images by leveraging the annotation of center positions for pedestrians' heads. Promising progresses have been made with the prevalence of deep Convolutional Neural Networks. Existing methods widely employ the Euclidean distance (i.e., $L_2$ loss) to optimize the model, which, however, has two main drawbacks: (1) the loss has difficulty in learning the spatial awareness (i.e., the position of head) since it struggles to retain the high-frequency variation in the density map, and (2) the loss is highly sensitive to various noises in crowd counting, such as the zero-mean noise, head size changes, and occlusions. Although the Maximum Excess over SubArrays (MESA) loss has been previously proposed to address the above issues by finding the rectangular subregion whose predicted density map has the maximum difference from the ground truth, it cannot be solved by gradient descent, thus can hardly be integrated into the deep learning framework. In this paper, we present a novel architecture called SPatial Awareness Network (SPANet) to incorporate spatial context for crowd counting. The Maximum Excess over Pixels (MEP) loss is proposed to achieve this by finding the pixel-level subregion with high discrepancy to the ground truth. To this end, we devise a weakly supervised learning scheme to generate such region with a multi-branch architecture. The proposed framework can be integrated into existing deep crowd counting methods and is end-to-end trainable. Extensive experiments on four challenging benchmarks show that our method can significantly improve the performance of baselines. More remarkably, our approach outperforms the state-of-the-art methods on all benchmark 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"
    - "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"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-2 (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 from synthetic data for crowd counting in the wild"
    - "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-3 (induced by source paper "fast video crowd counting with a temporal aware network"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "attend to count crowd counting with adaptive capacity multi scale cnns"
    - "crowd counting with deep structured scale integration network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "adaptive scenario discovery for crowd counting"

Connected through hyperedge HE-4 (induced by source paper "deep hough transform for semantic line detection"):
  with members:
    - "ppgnet learning point pair graph for line segment detection"
    - "cornernet detecting objects as paired keypoints"
    - "context aware crowd counting"
    - "ccnet criss cross attention for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "non local neural networks"
    - "feature pyramid networks for object detection"
    - "pyramid scene parsing network"

Connected through hyperedge HE-5 (induced by source paper "using depth for pixel wise detection of adversarial attacks in crowd counting"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "bayesian loss for crowd count estimation with point supervision"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "attacking optical flow"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "counting with focus for free"
    - "learn to scale generating multipolar normalized density map for crowd counting"

Connected through hyperedge HE-6 (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 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"
    - "context aware crowd counting"

Connected through hyperedge HE-7 (induced by source paper "estimating people flows to better count them in crowded scenes"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "counting with focus for free"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "perspective guided convolution networks for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"

Connected through hyperedge HE-8 (induced by source paper "improving the learning of multi column convolutional neural network for crowd counting"):
  with members:
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "stacked pooling improving crowd counting by boosting scale invariance"
    - "in defense of single column networks for crowd counting"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "context aware crowd counting"
    - "adaptive scenario discovery for crowd counting"
    - "padnet pan density crowd counting"
    - "top down feedback for crowd counting convolutional neural network"

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)