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: "from open set to closed set counting objects by spatial divide and conquer"
  Abstract: Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in $[0,+\infty)$ in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and- Conquer Network (S-DCNet). S-DCNet only learns from a closed set but can generalize well to open-set scenarios via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github. com/xhp-hust-2018-2011/S-DCNet.

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"
    - "learning spatial awareness to improve crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
    - "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 spatial awareness to improve crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "crowd counting with deep structured scale integration network"
    - "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 "plug and play rescaling based crowd counting in static images"):
  with members:
    - "zoomcount a zooming mechanism for crowd counting in static images"
    - "counting with focus for free"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "object detection with convolutional neural networks"
    - "mdfn multi scale deep feature learning network for object detection"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"

Connected through hyperedge HE-4 (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"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"

Connected through hyperedge HE-5 (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 spatial awareness to improve crowd counting"
    - "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"
    - "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-6 (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"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "perspective guided convolution networks 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)