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 semantic segmentation of natural and medical images a review"
  Abstract: The (medical) image semantic segmentation task consists of classifying each pixel of an image (or just several ones) into an instance, where each instance (or category) corresponding to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the main deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural improvements, data synthesis-based, loss function-based improvements, sequenced models, weakly supervised, and multi-task methods and further for each group we analyzed each variant of these groups and discuss limitations of the current approaches and future research directions for semantic image segmentation.

Connected through hyperedge HE-1 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"):
  with members:
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "time series anomaly detection service at microsoft"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "deeply supervised salient object detection with short connections"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "robuststl a robust seasonal trend decomposition algorithm for long time series"

Connected through hyperedge HE-2 (induced by source paper "trust your model iterative label improvement and robust training by confidence based filtering and dataset partitioning"):
  with members:
    - "self learning to filter noisy labels with self ensembling"
    - "training object detectors with noisy data"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "robust learning under label noise with iterative noise filtering"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "learning to reweight examples for robust deep learning"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "understanding deep learning requires rethinking generalization"

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)