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: "discovery radiomics via stochasticnet sequencers for cancer detection"
  Abstract: Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability to characterize unique cancer traits. In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data. In particular, we leverage novel StochasticNet radiomic sequencers for extracting custom radiomic features tailored for characterizing unique cancer tissue phenotype. Using StochasticNet radiomic sequencers discovered using a wealth of lung CT data, we perform binary classification on 42,340 lung lesions obtained from the CT scans of 93 patients in the LIDC-IDRI dataset. Preliminary results show significant improvement over previous state-of-the-art methods, indicating the potential of the proposed discovery radiomics framework for improving cancer screening and diagnosis.

Connected through hyperedge HE-1 (induced by source paper "sisc end to end interpretable discovery radiomics driven lung cancer prediction via stacked interpretable sequencing cells"):
  with members:
    - "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "diagnostic classification of lung nodules using 3d neural networks"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "opening the black box of financial ai with clear trade a class enhanced attentive response approach for explaining and visualizing deep learning driven stock market prediction"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "characterization of lung nodule malignancy using hybrid shape and appearance features"

Connected through hyperedge HE-2 (induced by source paper "discovery radiomics with clear dr interpretable computer aided diagnosis of diabetic retinopathy"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "learning deep features for discriminative localization"
    - "striving for simplicity the all convolutional net"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"):
  with members:
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
    - "stochasticnet forming deep neural networks via stochastic connectivity"

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)