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: "classification of hepatic lesions using the matching metric"
  Abstract: In this paper we present a methodology of classifying hepatic (liver) lesions using multidimensional persistent homology, the matching metric (also called the bottleneck distance), and a support vector machine. We present our classification results on a dataset of 132 lesions that have been outlined and annotated by radiologists. We find that topological features are useful in the classification of hepatic lesions. We also find that two-dimensional persistent homology outperforms one-dimensional persistent homology in this application.

Connected through hyperedge HE-1 (induced by source paper "end to end discriminative deep network for liver lesion classification"):
  with members:
    - "liver lesion segmentation informed by joint liver segmentation"
    - "a survey on deep learning in medical image analysis"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"):
  with members:
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "towards adversarial retinal image synthesis"
    - "virtual pet images from ct data using deep convolutional networks initial results"
    - "a survey on deep learning in medical image analysis"
    - "scan structure correcting adversarial network for organ segmentation in chest x rays"
    - "medical image synthesis with context aware generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "a kernel for multi parameter persistent homology"):
  with members:
    - "sliced wasserstein kernel for persistence diagrams"
    - "a persistence landscapes toolbox for topological statistics"
    - "deep learning with topological signatures"
    - "the theory of the interleaving distance on multidimensional persistence modules"
    - "the rank invariant stability via interleavings"

Connected through hyperedge HE-4 (induced by source paper "the theory of the interleaving distance on multidimensional persistence modules"):
  with members:
    - "categorified reeb graphs"
    - "natural pseudo distance and optimal matching between reduced size functions"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-5 (induced by source paper "the rank invariant stability via interleavings"):
  with members:
    - "the theory of the interleaving distance on multidimensional persistence modules"
    - "computing multidimensional persistence"

Connected through hyperedge HE-6 (induced by source paper "persistence modules algebra and algorithms"):
  with members:
    - "a spectral sequence for parallelized persistence"

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)