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: "dirichlet based gaussian processes for large scale calibrated classification"
  Abstract: In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled approach, but the corresponding computational burden is hardly sustainable in large-scale problems and devising efficient alternatives is a challenge. In this work, we investigate if and how Gaussian process regression directly applied to the classification labels can be used to tackle this question. While in this case training time is remarkably faster, predictions need be calibrated for classification and uncertainty estimation. To this aim, we propose a novel approach based on interpreting the labels as the output of a Dirichlet distribution. Extensive experimental results show that the proposed approach provides essentially the same accuracy and uncertainty quantification of Gaussian process classification while requiring only a fraction of computational resources.

Connected through hyperedge HE-1 (induced by source paper "mix n match ensemble and compositional methods for uncertainty calibration in deep learning"):
  with members:
    - "verified uncertainty calibration"
    - "evaluating model calibration in classification"
    - "when does label smoothing help"
    - "measuring calibration in deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "non parametric calibration for classification"
    - "pytorch an imperative style high performance deep learning library"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"

Connected through hyperedge HE-2 (induced by source paper "calibration of deep probabilistic models with decoupled bayesian neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "ergodic measure preserving flows"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "inference suboptimality in variational autoencoders"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "beyond temperature scaling obtaining well calibrated multiclass probabilities with dirichlet calibration"):
  with members:
    - "verified uncertainty calibration"
    - "evaluating model calibration in classification"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"
    - "wide residual networks"

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)