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 forests"
  Abstract: Label distribution learning (LDL) is a general learning framework, which assigns to an instance a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning, e.g., to learn deep features in an end-to-end manner. This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions. 2) The learning of differentiable decision trees can be combined with representation learning. We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding. The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.

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"
    - "interpretable convolutional neural networks"
    - "deep regression forests for age estimation"

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"
    - "deep regression forests for age estimation"
    - "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:
    - "deep regression forests for age estimation"
    - "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"
    - "deep regression forests for age estimation"
    - "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"
    - "deep regression forests for age estimation"
    - "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 "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"):
  with members:
    - "deep sets"
    - "saliency based sequential image attention with multiset prediction"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "deep supervised discrete hashing"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "structured generative adversarial networks"
    - "first order adaptive sample size methods to reduce complexity of empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "deep regression forests for age estimation"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "r fcn object detection via region based fully convolutional networks"
    - "some like it hot visual guidance for preference prediction"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "label distribution learning"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

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)