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: "universum prescription regularization using unlabeled data"
  Abstract: This paper shows that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning. We call it universum prescription by the fact that the prescribed labels cannot be one of the supervised labels. In spite of its simplicity, universum prescription obtained competitive results in training deep convolutional networks for CIFAR-10, CIFAR-100, STL-10 and ImageNet datasets. A qualitative justification of these approaches using Rademacher complexity is presented. The effect of a regularization parameter -- probability of sampling from unlabeled data -- is also studied empirically.

Connected through hyperedge HE-1 (induced by source paper "practical solutions for machine learning safety in autonomous vehicles"):
  with members:
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "selectivenet a deep neural network with an integrated reject option"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "using pre training can improve model robustness and uncertainty"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "training verified learners with learned verifiers"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-2 (induced by source paper "on exploring undetermined relationships for visual relationship detection"):
  with members:
    - "efficient training for positive unlabeled learning"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting and recognizing human object interactions"
    - "neural motifs scene graph parsing with global context"
    - "relation networks for object detection"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-3 (induced by source paper "multiclass universum svm"):
  with members:
    - "universum learning for svm regression"
    - "universum learning for multiclass svm"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples detection in deep networks with convolutional filter statistics"):
  with members:
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "learning to abstain from binary prediction"
    - "adversarial examples in the physical world"
    - "foveation based mechanisms alleviate adversarial examples"
    - "towards open set deep networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning with a strong adversary"

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)