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: "a comprehensive analysis of deep regression"
  Abstract: Deep learning revolutionized data science, and recently, its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks such as human pose estimation did not escape this methodological change. The large number of deep architectures lead to a plethora of methods that are evaluated under different experimental protocols. Moreover, small changes in the architecture of the network, or in the data pre-processing procedure, together with the stochastic nature of the optimization methods, lead to notably different results, making extremely difficult to sift methods that significantly outperform others. Therefore, when proposing regression algorithms, practitioners proceed by trial-and-error. This situation motivated the current study, in which we perform a systematic evaluation and a statistical analysis of the performance of vanilla deep regression -- short for convolutional neural networks with a linear regression top layer --. Up to our knowledge this is the first comprehensive analysis of deep regression techniques. We perform experiments on three vision problems and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture.

Connected through hyperedge HE-1 (induced by source paper "cahphf context aware hierarchical qos prediction with hybrid filtering"):
  with members:
    - "a privacy preserving qos prediction framework for web service recommendation"

Connected through hyperedge HE-2 (induced by source paper "learning deep conditional target densities for accurate regression"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "deep high resolution representation learning for human pose estimation"
    - "bottom up object detection by grouping extreme and center points"
    - "lightweight probabilistic deep networks"
    - "cornernet detecting objects as paired keypoints"
    - "atom accurate tracking by overlap maximization"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "bounding box regression with uncertainty for accurate object detection"

Connected through hyperedge HE-3 (induced by source paper "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"

Connected through hyperedge HE-4 (induced by source paper "on dropout and nuclear norm regularization"):
  with members:
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the implicit bias of dropout"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "deep linear neural networks with arbitrary loss all local minima are global"

Connected through hyperedge HE-5 (induced by source paper "intra variable handwriting inspection reinforced with idiosyncrasy analysis"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "in defense of the triplet loss for person re identification"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "recurrent models of visual attention"
    - "imagenet large scale visual recognition challenge"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "data driven approximation of parametrized pdes by reduced basis and neural networks"):
  with members:
    - "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "recent trends in deep learning based natural language processing"
    - "optimal approximation with sparsely connected deep neural networks"
    - "residual attention network for image classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "deepgum learning deep robust regression with a gaussian uniform mixture model"):
  with members:
    - "viraliency pooling local virality"
    - "how to make an image more memorable a deep style transfer approach"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "learning from noisy labels with distillation"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "fashion landmark detection in the wild"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "robust optimization for deep regression"

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)