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: "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
  Abstract: The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets. Nevertheless, unsupervised image categorization (i.e., without the ground-truth labeling) is much less investigated, yet critically important and difficult when annotations are extremely hard to obtain in the conventional way of "Google Search" and crowd sourcing. We address this problem by presenting a looped deep pseudo-task optimization (LDPO) framework for joint mining of deep CNN features and image labels. Our method is conceptually simple and rests upon the hypothesized "convergence" of better labels leading to better trained CNN models which in turn feed more discriminative image representations to facilitate more meaningful clusters/labels. Our proposed method is validated in tackling two important applications: 1) Large-scale medical image annotation has always been a prohibitively expensive and easily-biased task even for well-trained radiologists. Significantly better image categorization results are achieved via our proposed approach compared to the previous state-of-the-art method. 2) Unsupervised scene recognition on representative and publicly available datasets with our proposed technique is examined. The LDPO achieves excellent quantitative scene classification results. On the MIT indoor scene dataset, it attains a clustering accuracy of 75.3%, compared to the state-of-the-art supervised classification accuracy of 81.0% (when both are based on the VGG-VD model).

Connected through hyperedge HE-1 (induced by source paper "lesion harvester iteratively mining unlabeled lesions and hard negative examples at scale"):
  with members:
    - "attentive ct lesion detection using deep pyramid inference with multi scale booster"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"
    - "objects as points"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "soft sampling for robust object detection"
    - "focal loss for dense object detection"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-2 (induced by source paper "collaborative learning of semi supervised clustering and classification for labeling uncurated data"):
  with members:
    - "an analytical workflow for clustering forensic images"
    - "hard sample mining for the improved retraining of automatic speech recognition"
    - "exploring the limits of weakly supervised pretraining"
    - "cnn features are also great at unsupervised classification"
    - "photo guided exploration of volume data features"
    - "joint unsupervised learning of deep representations and image clusters"

Connected through hyperedge HE-3 (induced by source paper "unsupervised deep clustering for predictive texture pattern discovery in medical images"):
  with members:
    - "unsupervised segmentation of 3d medical images based on clustering and deep representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised pre training of image features on non curated data"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "revisiting self supervised visual representation learning"
    - "rethinking imagenet pre training"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "cross pixel optical flow similarity for self supervised learning"

Connected through hyperedge HE-5 (induced by source paper "deep comprehensive correlation mining for image clustering"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "differentiable linearized admm"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "essential tensor learning for multi view spectral clustering"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-6 (induced by source paper "automated enriched medical concept generation for chest x ray images"):
  with members:
    - "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"
    - "on the automatic generation of medical imaging reports"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "deep residual learning for image recognition"
    - "interleaved text image deep mining on a large scale radiology database for automated image interpretation"

Connected through hyperedge HE-7 (induced by source paper "improving image clustering with multiple pretrained cnn feature extractors"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "cnn features are also great at unsupervised classification"
    - "a survey on multi view clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "xception deep learning with depthwise separable convolutions"
    - "joint unsupervised learning of deep representations and image clusters"
    - "deep residual learning for image recognition"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-8 (induced by source paper "unsupervised robotic sorting towards autonomous decision making robots"):
  with members:
    - "semantically meaningful view selection"
    - "discriminatively boosted image clustering with fully convolutional auto encoders"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "xception deep learning with depthwise separable convolutions"
    - "joint unsupervised learning of deep representations and image clusters"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "multimodal deep learning for robust rgb d object 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.CV(Computer Vision and Pattern Recognition)