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: "brain tumor segmentation based on refined fully convolutional neural networks with a hierarchical dice loss"
  Abstract: As a basic task in computer vision, semantic segmentation can provide fundamental information for object detection and instance segmentation to help the artificial intelligence better understand real world. Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization. In this paper, we apply several famous FCNN to brain tumor segmentation, making comparisons and adjusting network architectures to achieve better performance measured by metrics such as precision, recall, mean of intersection of union (mIoU) and dice score coefficient (DSC). The adjustments to the classic FCNN include adding more connections between convolutional layers, enlarging decoders after up sample layers and changing the way shallower layers' information is reused. Besides the structure modification, we also propose a new classifier with a hierarchical dice loss. Inspired by the containing relationship between classes, the loss function converts multiple classification to multiple binary classification in order to counteract the negative effect caused by imbalance data set. Massive experiments have been done on the training set and testing set in order to assess our refined fully convolutional neural networks and new types of loss function. Competitive figures prove they are more effective than their predecessors.

Connected through hyperedge HE-1 (induced by source paper "towards interpretable and robust hand detection via pixel wise prediction"):
  with members:
    - "scale invariant fully convolutional network detecting hands efficiently"
    - "east an efficient and accurate scene text detector"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "simple online and realtime tracking with a deep association metric"
    - "simple online and realtime tracking"
    - "ssd single shot multibox detector"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "guided attention network for object detection and counting on drones"):
  with members:
    - "reppoints point set representation for object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "non local neural networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "adaptive class weight based dual focal loss for improved semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "focal loss for dense object detection"
    - "east an efficient and accurate scene text detector"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "efficient yet deep convolutional neural networks for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "scale invariant fully convolutional network detecting hands efficiently"):
  with members:
    - "east an efficient and accurate scene text detector"
    - "ssd single shot multibox detector"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

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)