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 learning for delineation of curvilinear structures"
  Abstract: Many recent delineation techniques owe much of their increased effectiveness to path classification algorithms that make it possible to distinguish promising paths from others. The downside of this development is that they require annotated training data, which is tedious to produce. #R##N#In this paper, we propose an Active Learning approach that considerably speeds up the annotation process. Unlike standard ones, it takes advantage of the specificities of the delineation problem. It operates on a graph and can reduce the training set size by up to 80% without compromising the reconstruction quality. #R##N#We will show that our approach outperforms conventional ones on various biomedical and natural image datasets, thus showing that it is broadly applicable.

Connected through hyperedge HE-1 (induced by source paper "geometry in active learning for binary and multi class image segmentation"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning algorithms for active learning"
    - "annotating object instances with a polygon rnn"
    - "click carving segmenting objects in video with point clicks"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "becoming the expert interactive multi class machine teaching"
    - "what s the point semantic segmentation with point supervision"
    - "material recognition in the wild with the materials in context database"

Connected through hyperedge HE-2 (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-3 (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"
    - "introducing geometry in active learning for image segmentation"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "learning active learning from data"):
  with members:
    - "value iteration networks"
    - "fully convolutional networks for semantic segmentation"
    - "active testing for face detection and localization"

Connected through hyperedge HE-5 (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)