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 learning under privileged information using heteroscedastic dropout"
  Abstract: Unlike machines, humans learn through rapid, abstract model-building. The role of a teacher is not simply to hammer home right or wrong answers, but rather to provide intuitive comments, comparisons, and explanations to a pupil. This is what the Learning Under Privileged Information (LUPI) paradigm endeavors to model by utilizing extra knowledge only available during training. We propose a new LUPI algorithm specifically designed for Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We propose to use a heteroscedastic dropout (i.e. dropout with a varying variance) and make the variance of the dropout a function of privileged information. Intuitively, this corresponds to using the privileged information to control the uncertainty of the model output. We perform experiments using CNNs and RNNs for the tasks of image classification and machine translation. Our method significantly increases the sample efficiency during learning, resulting in higher accuracy with a large margin when the number of training examples is limited. We also theoretically justify the gains in sample efficiency by providing a generalization error bound decreasing with $O(\frac{1}{n})$, where $n$ is the number of training examples, in an oracle case.

Connected through hyperedge HE-1 (induced by source paper "privileged features distillation at taobao recommendations"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "large scale distributed neural network training through online distillation"
    - "self attentive sequential recommendation"
    - "deep mutual learning"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-2 (induced by source paper "hard pixel mining for depth privileged semantic segmentation"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "tradi tracking deep neural network weight distributions"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "lca loss change allocation for neural network training"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "ionet learning to cure the curse of drift in inertial odometry"

Connected through hyperedge HE-4 (induced by source paper "privileged features distillation for e commerce recommendations"):
  with members:
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "large scale distributed neural network training through online distillation"
    - "modality distillation with multiple stream networks for action recognition"
    - "self attentive sequential recommendation"
    - "deep mutual learning"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-5 (induced by source paper "ockelm kernel extreme learning machine based one class classification using privileged information or koc kernel ridge regression or least square svm with zero bias based one class classification using privileged information"):
  with members:
    - "privileged information for data clustering"
    - "when is there a representer theorem vector versus matrix regularizers"

Connected through hyperedge HE-6 (induced by source paper "discriminative multi view privileged information learning for image re ranking"):
  with members:
    - "multi view intact space learning"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "aggregating deep convolutional features for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "privileged information for data clustering"

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)