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: "a tractable inference algorithm for diagnosing multiple diseases"
  Abstract: We examine a probabilistic model for the diagnosis of multiple diseases. In the model, diseases and findings are represented as binary variables. Also, diseases are marginally independent, features are conditionally independent given disease instances, and diseases interact to produce findings via a noisy OR-gate. An algorithm for computing the posterior probability of each disease, given a set of observed findings, called quickscore, is presented. The time complexity of the algorithm is O(nm-2m+), where n is the number of diseases, m+ is the number of positive findings and m- is the number of negative findings. Although the time complexity of quickscore i5 exponential in the number of positive findings, the algorithm is useful in practice because the number of observed positive findings is usually far less than the number of diseases under consideration. Performance results for quickscore applied to a probabilistic version of Quick Medical Reference (QMR) are provided.

Connected through hyperedge HE-1 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "multiple instance learning convolutional neural networks for object recognition"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-2 (induced by source paper "universal marginalizer for amortised inference and embedding of generative models"):
  with members:
    - "deep amortized inference for probabilistic programs"
    - "adam a method for stochastic optimization"
    - "loopy belief propagation for approximate inference an empirical study"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised dense video captioning"):
  with members:
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "beyond caption to narrative video captioning with multiple sentences"
    - "captioning images with diverse objects"
    - "jointly modeling embedding and translation to bridge video and language"
    - "you only look once unified real time object detection"
    - "the long short story of movie description"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"

Connected through hyperedge HE-4 (induced by source paper "aenet learning deep audio features for video analysis"):
  with members:
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "video2gif automatic generation of animated gifs from video"
    - "beyond short snippets deep networks for video classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-5 (induced by source paper "on the semantics and complexity of probabilistic logic programs"):
  with members:
    - "a new approach to updating beliefs"
    - "continuous time bayesian networks"
    - "probabilistic reasoning with answer sets"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-6 (induced by source paper "variational hybridization and transformation for large inaccurate noisy or networks"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "addressing the rare word problem in neural machine translation"
    - "loopy belief propagation for approximate inference an empirical study"
    - "unsupervised learning of noisy or bayesian networks"
    - "formula based probabilistic inference"
    - "structured priors for structure learning"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-7 (induced by source paper "deep convolutional neural networks and data augmentation for acoustic event detection"):
  with members:
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "very deep convolutional networks for large scale image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "doctor ai predicting clinical events via recurrent neural networks"):
  with members:
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "deep learning in neural networks"
    - "visualizing and understanding recurrent networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "how transferable are features in deep neural networks"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "lsda large scale detection through adaptation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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.AI(Artificial Intelligence)