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: "reducing uncertainty in undersampled mri reconstruction with active acquisition"
  Abstract: The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements. This partial view naturally induces reconstruction uncertainty that can only be reduced by acquiring additional measurements. In this paper, we present a novel method for MRI reconstruction that, at inference time, dynamically selects the measurements to take and iteratively refines the prediction in order to best reduce the reconstruction error and, thus, its uncertainty. We validate our method on a large scale knee MRI dataset, as well as on ImageNet. Results show that (1) our system successfully outperforms active acquisition baselines; (2) our uncertainty estimates correlate with error maps; and (3) our ResNet-based architecture surpasses standard pixel-to-pixel models in the task of MRI reconstruction. The proposed method not only shows high-quality reconstructions but also paves the road towards more applicable solutions for accelerating MRI.

Connected through hyperedge HE-1 (induced by source paper "miopen an open source library for deep learning primitives"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "simple recurrent units for highly parallelizable recurrence"
    - "imagenet training in minutes"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-2 (induced by source paper "training free uncertainty estimation for neural networks"):
  with members:
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deeper depth prediction with fully convolutional residual networks"
    - "loss aware binarization of deep networks"
    - "natural parameter networks a class of probabilistic neural 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.CV(Computer Vision and Pattern Recognition)