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: "deep regression forests for age estimation"
  Abstract: Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is heterogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging patterns. In this paper, we propose Deep Regression Forests (DRFs), an end-to-end model, for age estimation. DRFs connect the split nodes to a fully connected layer of a convolutional neural network (CNN) and deal with heterogeneous data by jointly learning input-dependant data partitions at the split nodes and data abstractions at the leaf nodes. This joint learning follows an alternating strategy: First, by fixing the leaf nodes, the split nodes as well as the CNN parameters are optimized by Back-propagation; Then, by fixing the split nodes, the leaf nodes are optimized by iterating a step-size free and fast-converging update rule derived from Variational Bounding. We verify the proposed DRFs on three standard age estimation benchmarks and achieve state-of-the-art results on all of them.

Connected through hyperedge HE-1 (induced by source paper "robust regression via deep negative correlation learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "bag of tricks for image classification with convolutional neural networks"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-2 (induced by source paper "facial age estimation by deep residual decision making"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "visualizing the decision making process in deep neural decision forest"
    - "interpreting cnns via decision trees"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "visualizing the loss landscape of neural nets"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "label distribution learning forests"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "self paced deep regression forests for facial age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "soft ranking label encoding for robust facial age estimation"
    - "active self paced learning for cost effective and progressive face identification"
    - "self paced multi task clustering"
    - "label distribution learning forests"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "bridgenet a continuity aware probabilistic network for age estimation"):
  with members:
    - "label distribution learning forests"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "deep differentiable random forests for age estimation"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "label distribution learning forests"
    - "decision forests convolutional networks and the models in between"
    - "some like it hot visual guidance for preference prediction"
    - "label distribution learning"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "ordinal distribution regression for gait based age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "label distribution learning forests"
    - "squared earth mover s distance based loss for training deep neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "visualizing the decision making process in deep neural decision forest"):
  with members:
    - "interpreting cnns via decision trees"
    - "interpretable convolutional neural networks"
    - "a fast and accurate unconstrained face detector"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "a coupled evolutionary network for age estimation"):
  with members:
    - "quantifying facial age by posterior of age comparisons"
    - "deep label distribution learning with label ambiguity"
    - "apparent age estimation using ensemble of deep learning models"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "a light cnn for deep face representation with noisy labels"
    - "label distribution learning"

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)