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: "detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications"
  Abstract: Automated detection of sclerotic metastases (bone lesions) in Computed Tomography (CT) images has potential to be an important tool in clinical practice and research. State-of-the-art methods show performance of 79% sensitivity or true-positive (TP) rate, at 10 false-positives (FP) per volume. We design a two-tiered coarse-to-fine cascade framework to first operate a highly sensitive candidate generation system at a maximum sensitivity of ~92% but with high FP level (~50 per patient). Regions of interest (ROI) for lesion candidates are generated in this step and function as input for the second tier. In the second tier we generate N 2D views, via scale, random translations, and rotations with respect to each ROI centroid coordinates. These random views are used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. We validate the approach on CT images of 59 patients (49 with sclerotic metastases and 10 normal controls). The proposed method reduces the number of FP/vol. from 4 to 1.2, 7 to 3, and 12 to 9.5 when comparing a sensitivity rates of 60%, 70%, and 80% respectively in testing. The Area-Under-the-Curve (AUC) is 0.834. The results show marked improvement upon previous work.

Connected through hyperedge HE-1 (induced by source paper "deep learning in bioinformatics"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep belief networks used on high resolution multichannel electroencephalography data for seizure detection"
    - "deep motif visualizing genomic sequence classifications"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "comparative study of deep learning software frameworks"
    - "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"
    - "deepspark spark based deep learning supporting asynchronous updates and caffe compatibility"
    - "deepspark a spark based distributed deep learning framework for commodity clusters"

Connected through hyperedge HE-2 (induced by source paper "improving computer aided detection using convolutional neural networks and random view aggregation"):
  with members:
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"
    - "2d view aggregation for lymph node detection using a shallow hierarchy of linear classifiers"
    - "a new 2 5d representation for lymph node detection using random sets of deep convolutional neural network observations"
    - "two stream convolutional networks for action recognition in videos"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional networks for pancreas segmentation in ct imaging"):
  with members:
    - "one weird trick for parallelizing convolutional neural networks"
    - "a new 2 5d representation for lymph node detection using random sets of deep convolutional neural network observations"
    - "a bottom up approach for automatic pancreas segmentation in abdominal ct scans"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "anatomy specific classification of medical images using deep convolutional nets"):
  with members:
    - "one weird trick for parallelizing convolutional neural networks"
    - "a new 2 5d representation for lymph node detection using random sets of deep convolutional neural network observations"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)