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: "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
  Abstract: Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manually intensive delineation of radiosensitive organs at risk (OARs). This planning process can delay treatment commencement. While auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying and achieving expert performance remain. Adopting a deep learning approach, we demonstrate a 3D U-Net architecture that achieves performance similar to experts in delineating a wide range of head and neck OARs. The model was trained on a dataset of 663 deidentified computed tomography (CT) scans acquired in routine clinical practice and segmented according to consensus OAR definitions. We demonstrate its generalisability through application to an independent test set of 24 CT scans available from The Cancer Imaging Archive collected at multiple international sites previously unseen to the model, each segmented by two independent experts and consisting of 21 OARs commonly segmented in clinical practice. With appropriate validation studies and regulatory approvals, this system could improve the effectiveness of radiotherapy pathways.

Connected through hyperedge HE-1 (induced by source paper "observer variation aware medical image segmentation by combining deep learning and surrogate assisted genetic algorithms"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "ce net context encoder network for 2d medical image segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"
    - "the microsoft 2017 conversational speech recognition system"

Connected through hyperedge HE-3 (induced by source paper "a hierarchical probabilistic u net for modeling multi scale ambiguities"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "tensormask a foundation for dense object segmentation"
    - "instance segmentation of biological images using harmonic embeddings"
    - "semantic image synthesis with spatially adaptive normalization"
    - "instance segmentation by deep coloring"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"

Connected through hyperedge HE-4 (induced by source paper "atlas based segmentations via semi supervised diffeomorphic registrations"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised 3d end to end medical image registration with volume tweening network"
    - "infovae information maximizing variational autoencoders"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "mask rcnn and u net ensembled for nuclei segmentation"):
  with members:
    - "mask r cnn"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "scikit image image processing in python"

Connected through hyperedge HE-6 (induced by source paper "automated image registration quality assessment utilizing deep learning based ventricle extraction in clinical data"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "multi atlas segmentation of biomedical images a survey"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "machine learning for automatic construction of pseudo realistic pediatric abdominal phantoms"):
  with members:
    - "improving model based genetic programming for symbolic regression of small expressions"

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)