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 evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"
  Abstract: While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features which may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose a novel evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically-proven diagnostic data from the LIDC-IDRI dataset. The evolved deep radiomic sequencer shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

Connected through hyperedge HE-1 (induced by source paper "from handcrafted to deep learning based cancer radiomics challenges and opportunities"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "brain tumor type classification via capsule networks"
    - "dynamic routing between capsules"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "prostate cancer diagnosis using deep learning with 3d multiparametric mri"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"

Connected through hyperedge HE-2 (induced by source paper "nature vs nurture the role of environmental resources in evolutionary deep intelligence"):
  with members:
    - "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks"
    - "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face 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.NE(Neural and Evolutionary Computing)