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: "deconvolutional feature stacking for weakly supervised semantic segmentation"
  Abstract: A weakly-supervised semantic segmentation framework with a tied deconvolutional neural network is presented. Each deconvolution layer in the framework consists of unpooling and deconvolution operations. 'Unpooling' upsamples the input feature map based on unpooling switches defined by corresponding convolution layer's pooling operation. 'Deconvolution' convolves the input unpooled features by using convolutional weights tied with the corresponding convolution layer's convolution operation. The unpooling-deconvolution combination helps to eliminate less discriminative features in a feature extraction stage, since output features of the deconvolution layer are reconstructed from the most discriminative unpooled features instead of the raw one. This results in reduction of false positives in a pixel-level inference stage. All the feature maps restored from the entire deconvolution layers can constitute a rich discriminative feature set according to different abstraction levels. Those features are stacked to be selectively used for generating class-specific activation maps. Under the weak supervision (image-level labels), the proposed framework shows promising results on lesion segmentation in medical images (chest X-rays) and achieves state-of-the-art performance on the PASCAL VOC segmentation dataset in the same experimental condition.

Connected through hyperedge HE-1 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

Connected through hyperedge HE-2 (induced by source paper "saliency guided deep network for weakly supervised image segmentation"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "self attention recurrent network for saliency detection"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "diverse sampling for self supervised learning of semantic segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-4 (induced by source paper "seed expand and constrain three principles for weakly supervised image segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "improving weakly supervised object localization by micro annotation"
    - "learning transferrable knowledge for semantic segmentation with deep convolutional neural network"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "learning deep features for discriminative localization"
    - "what s the point semantic segmentation with point supervision"
    - "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)