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 label distribution learning with label ambiguity"
  Abstract: Convolutional neural networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most important factors for its success. However, it is difficult to collect sufficient training images with precise labels in some domains, such as apparent age estimation, head pose estimation, multilabel classification, and semantic segmentation. Fortunately, there is ambiguous information among labels, which makes these tasks different from traditional classification. Based on this observation, we convert the label of each image into a discrete label distribution, and learn the label distribution by minimizing a Kullback–Leibler divergence between the predicted and ground-truth label distributions using deep ConvNets. The proposed deep label distribution learning (DLDL) method effectively utilizes the label ambiguity in both feature learning and classifier learning, which help prevent the network from overfitting even when the training set is small. Experimental results show that the proposed approach produces significantly better results than the state-of-the-art methods for age estimation and head pose estimation. At the same time, it also improves recognition performance for multi-label classification and semantic segmentation tasks.

Connected through hyperedge HE-1 (induced by source paper "non parametric uni modality constraints for deep ordinal classification"):
  with members:
    - "constrained deep networks lagrangian optimization via log barrier extensions"
    - "ordinal distribution regression for gait based age estimation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "min max entropy for weakly supervised pointwise localization"
    - "deep weakly supervised learning methods for classification and localization in histology images a survey"
    - "imposing hard constraints on deep networks promises and limitations"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"

Connected through hyperedge HE-2 (induced by source paper "neighborhood based pooling for population level label distribution learning"):
  with members:
    - "label propagation for deep semi supervised learning"
    - "label distribution learning"

Connected through hyperedge HE-3 (induced by source paper "c3ae exploring the limits of compact model for age estimation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "facial aging and rejuvenation by conditional multi adversarial autoencoder with ordinal regression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantifying facial age by posterior of age comparisons"
    - "random erasing data augmentation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "eeg based emotion recognition using regularized graph neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "transfer adaptation learning a decade survey"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "joint optimization framework for learning with noisy labels"
    - "a survey of unsupervised deep domain adaptation"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "probabilistic end to end noise correction for learning with noisy labels"):
  with members:
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "deep learning from noisy image labels with quality embedding"

Connected through hyperedge HE-6 (induced by source paper "soft ranking label encoding for robust facial age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "c3ae exploring the limits of compact model for age estimation"
    - "deep representation learning with part loss for person re identification"
    - "fine grained age estimation in the wild with attention lstm networks"
    - "a coupled evolutionary network for age estimation"
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-7 (induced by source paper "an end to end deep learning histochemical scoring system for breast cancer tma"):
  with members:
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "detecting cancer metastases on gigapixel pathology images"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "deep learning assessment of tumor proliferation in breast cancer histological images"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "facial pose estimation by deep learning from label distributions"):
  with members:
    - "a realistic face to face conversation system based on deep neural networks"
    - "video synthesis of human upper body with realistic face"
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "detecting gaze towards eyes in natural social interactions and its use in child assessment"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "fine grained head pose estimation without keypoints"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"

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)