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 short cut connections for object counting"
  Abstract: Object counting is an important task in computer vision due to its growing demand in applications such as traffic monitoring or surveillance. In this paper, we consider object counting as a learning problem of a joint feature extraction and pixel-wise object density estimation with Convolutional-Deconvolutional networks. We introduce a novel counting model, named Gated U-Net (GU-Net). Specifically, we propose to enrich the U-Net architecture with the concept of learnable short-cut connections. Standard short-cut connections are connections between layers in deep neural networks which skip at least one intermediate layer. Instead of simply setting short-cut connections, we propose to learn these connections from data. Therefore, our short-cuts can work as gating units, which optimize the flow of information between convolutional and deconvolutional layers in the U-Net architecture. We evaluate the introduced GU-Net architecture on three commonly used benchmark data sets for object counting. GU-Nets consistently outperform the base U-Net architecture, and achieve state-of-the-art performance.

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"
    - "learning from synthetic data for crowd counting in the wild"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning spatial fusion for single shot object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "adaptively connected neural networks"
    - "bag of freebies for training object detection neural networks"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "dafe fd density aware feature enrichment for face detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "uncertainty guided multi scale residual learning using a cycle spinning cnn for single image de raining"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"

Connected through hyperedge HE-4 (induced by source paper "nas fpn learning scalable feature pyramid architecture for object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "detnet a backbone network for object detection"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-5 (induced by source paper "enhanced 3d convolutional networks for crowd counting"):
  with members:
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "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"
    - "iterative crowd counting"
    - "an aggregated multicolumn dilated convolution network for perspective free counting"
    - "crowd counting by adaptively fusing predictions from an image pyramid"

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)