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 multi class learning from label proportions"
  Abstract: We propose a learning algorithm capable of learning from label proportions instead of direct data labels. In this scenario, our data are arranged into various bags of a certain size, and only the proportions of each label within a given bag are known. This is a common situation in cases where per-data labeling is lengthy, but a more general label is easily accessible. Several approaches have been proposed to learn in this setting with linear models in the multiclass setting, or with nonlinear models in the binary classification setting. Here we investigate the more general nonlinear multiclass setting, and compare two differentiable loss functions to train end-to-end deep neural networks from bags with label proportions. We illustrate the relevance of our methods on an image classification benchmark, and demonstrate the possibility to learn accurate image classifiers from bags of images.

Connected through hyperedge HE-1 (induced by source paper "learning from label proportions with consistency regularization"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning from label proportions for emphysema quantification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "co training for demographic classification using deep learning from label proportions"
    - "temporal ensembling for semi supervised learning"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "learning from label proportions with generative adversarial networks"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "good semi supervised learning that requires a bad gan"
    - "co training for demographic classification using deep learning from label proportions"
    - "perceptual losses for real time style transfer and super resolution"
    - "improved techniques for training gans"
    - "you only look once unified real time object detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "differentiable deep clustering with cluster size constraints"):
  with members:
    - "deep k means jointly clustering with k means and learning representations"
    - "improving gans using optimal transport"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "semi supervised learning with deep generative models"
    - "representation learning a review and new perspectives"
    - "learning probability measures with respect to optimal transport metrics"

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)