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: "active and continuous exploration with deep neural networks and expected model output changes"
  Abstract: The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet. In consequence, we need curious and continuously learning algorithms that actively acquire knowledge about semantic concepts which are present in available unlabeled data. As a step towards this goal, we show how to perform continuous active learning and exploration, where an algorithm actively selects relevant batches of unlabeled examples for annotation. These examples could either belong to already known or to yet undiscovered classes. Our algorithm is based on a new generalization of the Expected Model Output Change principle for deep architectures and is especially tailored to deep neural networks. Furthermore, we show easy-to-implement approximations that yield efficient techniques for active selection. Empirical experiments show that our method outperforms currently used heuristics.

Connected through hyperedge HE-1 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "yolov3 an incremental improvement"
    - "active learning for deep object detection"
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "automatically identifying wild animals in camera trap images with deep learning"

Connected through hyperedge HE-2 (induced by source paper "viewal active learning with viewpoint entropy for semantic segmentation"):
  with members:
    - "learning loss for active learning"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning loss for active learning"):
  with members:
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "active self paced learning for cost effective and progressive face identification"
    - "exploring the limits of weakly supervised pretraining"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "representation learning by learning to count"
    - "deep bayesian active learning with image data"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "multi modal active learning from human data a deep reinforcement learning approach"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "multimodal machine translation with reinforcement learning"
    - "active one shot learning"
    - "multimodal machine learning a survey and taxonomy"
    - "cost effective active learning for deep image classification"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"

Connected through hyperedge HE-5 (induced by source paper "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "deep active learning for named entity recognition"
    - "active learning for visual question answering an empirical study"
    - "deep bayesian active learning with image data"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "variational bayesian inference with stochastic search"

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)