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: "label distribution learning"
  Abstract: Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named \emph{label distribution learning} (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and multi-label learning as its special cases. This paper proposes six working LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare the performance of the LDL algorithms, six representative and diverse evaluation measures are selected via a clustering analysis, and the first batch of label distribution datasets are collected and made publicly available. Experimental results on one artificial and fifteen real-world datasets show clear advantages of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.

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"
    - "deep label distribution learning with label ambiguity"
    - "constrained deep weak supervision for histopathology image segmentation"

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"
    - "deep label distribution learning with label ambiguity"

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"
    - "deep label distribution learning with label ambiguity"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "on modelling label uncertainty in deep neural networks automatic estimation of intra observer variability in 2d echocardiography quality assessment"):
  with members:
    - "a machine learning framework for automatic reference free quality assessment in mri"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"
    - "on calibration of modern neural networks"
    - "deep label distribution learning with label ambiguity"
    - "comparative study of cnn and rnn for natural language processing"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "deep ordinal classification with inequality constraints"):
  with members:
    - "constrained deep networks lagrangian optimization via log barrier extensions"
    - "ordinal distribution regression for gait based age estimation"
    - "min max entropy for weakly supervised pointwise localization"
    - "imposing hard constraints on deep networks promises and limitations"
    - "deep label distribution learning with label ambiguity"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "deep differentiable random forests for age estimation"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "label distribution learning forests"
    - "deep regression forests for age estimation"
    - "decision forests convolutional networks and the models in between"
    - "some like it hot visual guidance for preference prediction"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "a snapshot on nonstandard supervised learning problems taxonomy relationships problem transformations and algorithm adaptations"):
  with members:
    - "dealing with difficult minority labels in imbalanced mutilabel data sets"
    - "multi instance multi label learning"
    - "multi label classification methods for multi target regression"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-8 (induced by source paper "latent distribution assumption for unbiased and consistent consensus modelling"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"

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.LG(Machine Learning)