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: "computational pathology challenges and promises for tissue analysis"
  Abstract: Abstract  The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised prostate tma classification via graph convolutional networks"):
  with members:
    - "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"
    - "bach grand challenge on breast cancer histology images"
    - "self attention graph pooling"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "representation learning with contrastive predictive coding"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "towards unsupervised cancer subtyping predicting prognosis using a histologic visual dictionary"):
  with members:
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"):
  with members:
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "improved inception residual convolutional neural network for object recognition"
    - "inception recurrent convolutional neural network for object recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "terabyte scale deep multiple instance learning for classification and localization in pathology"):
  with members:
    - "detecting cancer metastases on gigapixel pathology images"

Connected through hyperedge HE-5 (induced by source paper "mitochondria based renal cell carcinoma subtyping learning from deep vs flat feature representations"):
  with members:
    - "understanding neural networks through deep visualization"
    - "learning transferable features with deep adaptation networks"
    - "caffe convolutional architecture for fast feature embedding"

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)