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: "attention to head locations for crowd counting"
  Abstract: Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications. In this paper, we propose a novel method using an attention model to exploit head locations which are the most important cue for crowd counting. The attention model estimates a probability map in which high probabilities indicate locations where heads are likely to be present. The estimated probability map is used to suppress non-head regions in feature maps from several multi-scale feature extraction branches of a convolution neural network for crowd density estimation, which makes our method robust to complex backgrounds, scale variations and non-uniform distributions. In addition, we introduce a relative deviation loss to compensate a commonly used training loss, Euclidean distance, to improve the accuracy of sparse crowd density estimation. Experiments on Shanghai-Tech, UCF_CC_50 and World-Expo'10 data sets demonstrate the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "improving the learning of multi column convolutional neural network for crowd counting"):
  with members:
    - "learning spatial awareness to improve crowd counting"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning spatial awareness to improve crowd counting"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving the learning of multi column convolutional neural network for crowd counting"
    - "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"
    - "adaptive scenario discovery for crowd counting"
    - "padnet pan density crowd counting"
    - "leveraging unlabeled data for crowd counting by learning to rank"

Connected through hyperedge HE-3 (induced by source paper "segmentation guided attention network for crowd counting via curriculum learning"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "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"
    - "improving the learning of multi column convolutional neural network for crowd counting"

Connected through hyperedge HE-4 (induced by source paper "cccnet an attention based deep learning framework for categorized crowd counting"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "w net reinforced u net for density map estimation"
    - "crowd counting using scale aware attention networks"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd 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)