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: "precision learning towards use of known operators in neural networks"
  Abstract: In this paper, we consider the use of prior knowledge within neural networks. In particular, we investigate the effect of a known transform within the mapping from input data space to the output domain. We demonstrate that use of known transforms is able to change maximal error bounds. #R##N#In order to explore the effect further, we consider the problem of X-ray material decomposition as an example to incorporate additional prior knowledge. We demonstrate that inclusion of a non-linear function known from the physical properties of the system is able to reduce prediction errors therewith improving prediction quality from SSIM values of 0.54 to 0.88. #R##N#This approach is applicable to a wide set of applications in physics and signal processing that provide prior knowledge on such transforms. Also maximal error estimation and network understanding could be facilitated within the context of precision learning.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "deep generalized max pooling"):
  with members:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "semi supervised feature learning for improving writer identification"
    - "on the convergence of adam and beyond"
    - "in defense of the triplet loss for person re identification"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "encoding cnn activations for writer recognition"
    - "convolutional neural networks for font classification"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"

Connected through hyperedge HE-3 (induced by source paper "learning with known operators reduces maximum training error bounds"):
  with members:
    - "pyro nn python reconstruction operators in neural networks"
    - "visual object networks image generation with disentangled 3d representation"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "user loss a forced choice inspired approach to train neural networks directly by user interaction"
    - "deriving neural network architectures using precision learning parallel to fan beam conversion"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "a gentle introduction to deep learning in medical image processing"
    - "fast end to end trainable guided filter"

Connected through hyperedge HE-4 (induced by source paper "pyro nn python reconstruction operators in neural networks"):
  with members:
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "a gentle introduction to deep learning in medical image processing"
    - "image reconstruction by domain transform manifold learning"
    - "deep convolutional neural network for inverse problems in imaging"
    - "mr to x ray projection image synthesis"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "a divide and conquer approach towards understanding deep networks"):
  with members:
    - "learning with known operators reduces maximum training error bounds"
    - "fast end to end trainable guided filter"
    - "focal loss for dense object detection"
    - "fast image processing with fully convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a gentle introduction to deep learning in medical image processing"):
  with members:
    - "towards arbitrary noise augmentation deep learning for sampling from arbitrary probability distributions"
    - "metric driven learning of correspondence weighting for 2 d 3 d image registration"
    - "adversarial patch"
    - "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"
    - "image reconstruction via variational network for real time hand held sound speed imaging"
    - "deriving neural network architectures using precision learning parallel to fan beam conversion"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"

Connected through hyperedge HE-7 (induced by source paper "deriving neural network architectures using precision learning parallel to fan beam conversion"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "frangi net a neural network approach to vessel segmentation"
    - "precision learning reconstruction filter kernel discretization"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "metric driven learning of correspondence weighting for 2 d 3 d image registration"):
  with members:
    - "learning to find good correspondences"
    - "dilated fcn for multi agent 2d 3d medical image registration"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

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)