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: "cereals cost effective region based active learning for semantic segmentation"
  Abstract: State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images. However, gathering such a corpus is expensive, due to human annotation effort, in contrast to gathering unlabeled data. We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus. This minimizes human annotation effort while maximizing the performance of a semantic image segmentation method. The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting the spatial coherency of an image. The performance of CEREALS is demonstrated on Cityscapes, where we are able to reduce the annotation effort to 17%, while keeping 95% of the mean Intersection over Union (mIoU) of a model that was trained with the fully annotated training set of Cityscapes.

Connected through hyperedge HE-1 (induced by source paper "reinforced active learning for image segmentation"):
  with members:
    - "multi target embodied question answering"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "panoptic feature pyramid networks"
    - "learning a policy for opportunistic active learning"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "discovering general purpose active learning strategies"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "dsnet for real time driving scene semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "structured consistency loss for semi supervised semantic segmentation"):
  with members:
    - "global aggregation then local distribution in fully convolutional networks"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "consistency regularization and cutmix for semi supervised semantic segmentation"
    - "improving fast segmentation with teacher student learning"
    - "improving semantic segmentation via video propagation and label relaxation"

Connected through hyperedge HE-3 (induced by source paper "viewal active learning with viewpoint entropy for semantic segmentation"):
  with members:
    - "learning loss for active learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cost effective active learning for melanoma segmentation"
    - "cost effective active learning for deep image classification"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-4 (induced by source paper "deep active learning for efficient training of a lidar 3d object detector"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "localization aware active learning for object detection"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-5 (induced by source paper "parting with illusions about deep active learning"):
  with members:
    - "semi supervised semantic segmentation needs strong high dimensional perturbations"
    - "semi supervised semantic segmentation with high and low level consistency"
    - "rethinking deep active learning using unlabeled data at model training"
    - "variational adversarial active learning"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "unsupervised data augmentation for consistency training"
    - "learning loss for active learning"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-6 (induced by source paper "efficacy of pixel level ood detection for semantic segmentation"):
  with members:
    - "bayesian semantic instance segmentation in open set world"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "discriminative out of distribution detection for semantic segmentation"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"

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)