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: "introducing geometry in active learning for image segmentation"
  Abstract: We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes. To this end, we use these priors not only to select voxels most in need of annotation but to guarantee that they lie on 2D planar patch, which makes it much easier to annotate than if they were randomly distributed in the volume. A simplified version of this approach is effective in natural 2D images. We evaluated our approach on Electron Microscopy and Magnetic Resonance image volumes, as well as on natural images. Comparing our approach against several accepted baselines demonstrates a marked performance increase.

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"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "baod budget aware object detection"):
  with members:
    - "a flexible model for training action localization with varying levels of supervision"
    - "pcn part and context information for pedestrian detection with cnns"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "localization aware active learning for object detection"
    - "towards practical visual search engine within elasticsearch"
    - "generative adversarial active learning"
    - "learning active learning from data"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-3 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "variational adversarial active learning"
    - "automl a survey of the state of the art"
    - "are all training examples created equal an empirical study"
    - "how transferable are the datasets collected by active learners"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"

Connected through hyperedge HE-4 (induced by source paper "cereals cost effective region based active learning for semantic segmentation"):
  with members:
    - "adversarial learning for semi supervised semantic segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "learning from weak and noisy labels for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "mining deep and or object structures via cost sensitive question answer based active annotations"):
  with members:
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "yolov3 an incremental improvement"
    - "network transplanting"
    - "interpreting cnn knowledge via an explanatory graph"
    - "mining object parts from cnns via active question answering"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "bag level aggregation for multiple instance active learning in instance classification problems"):
  with members:
    - "multiple instance learning a survey of problem characteristics and applications"
    - "on measuring and quantifying performance error rates surrogate loss and an example in ssl"
    - "a multi scale multiple instance video description network"
    - "from captions to visual concepts and back"
    - "deep visual semantic alignments for generating image descriptions"
    - "multi instance learning by treating instances as non i i d samples"
    - "active learning with statistical models"

Connected through hyperedge HE-7 (induced by source paper "expected exponential loss for gaze based video and volume ground truth annotation"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "gaze2segment a pilot study for integrating eye tracking technology into medical image segmentation"
    - "early experiences with crowdsourcing airway annotations in chest ct"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "active learning for delineation of curvilinear structures"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "deep learning convolutional networks for multiphoton microscopy vasculature segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "theano a python framework for fast computation of mathematical expressions"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "sparse convolutional networks using the permutohedral lattice"
    - "an iterative convolutional neural network algorithm improves electron microscopy image segmentation"

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)