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 to count with deep object features"
  Abstract: Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self paced deep learning for weakly supervised object detection"
    - "visual interpretability for deep learning a survey"
    - "attention based deep multiple instance learning"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "deep learning from label proportions for emphysema quantification"
    - "weakly supervised salient object detection using image labels"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-2 (induced by source paper "crowd counting with decomposed uncertainty"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "iterative crowd counting"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"

Connected through hyperedge HE-3 (induced by source paper "toward automatic comparison of visualization techniques application to graph visualization"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "bigdl a distributed deep learning framework for big data"
    - "can the early human visual system compete with deep neural networks"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "xception deep learning with depthwise separable convolutions"
    - "25 years of cnns can we compare to human abstraction capabilities"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "textually enriched neural module networks for visual question answering"):
  with members:
    - "image labeling by assignment"
    - "dual attention networks for multimodal reasoning and matching"
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "exploring models and data for image question answering"

Connected through hyperedge HE-5 (induced by source paper "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"):
  with members:
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "an overview of multi task learning in deep neural networks"
    - "cross stitch networks for multi task learning"
    - "learning without forgetting"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "salient object subitizing"
    - "counting everyday objects in everyday scenes"

Connected through hyperedge HE-6 (induced by source paper "improving object counting with heatmap regulation"):
  with members:
    - "count ception counting by fully convolutional redundant counting"
    - "leveraging multiple datasets for deep leaf counting"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "drone based object counting by spatially regularized regional proposal network"
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "mask r cnn"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "end to end instance segmentation with recurrent attention"

Connected through hyperedge HE-7 (induced by source paper "subitizing with variational autoencoders"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "spatiotemporal modeling for crowd counting in videos"
    - "representation learning by learning to count"
    - "deep feature consistent variational autoencoder"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "counting everyday objects in everyday scenes"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "3d regression neural network for the quantification of enlarged perivascular spaces in brain mri"):
  with members:
    - "gp unet lesion detection from weak labels with a 3d regression network"
    - "segmentation of intracranial arterial calcification with deeply supervised residual dropout networks"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "end to end instance segmentation with recurrent attention"
    - "real time 2d 3d registration via cnn regression"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

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)