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: "semi and weakly supervised semantic segmentation using generative adversarial network"
  Abstract: Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs significant number of pixellevel annotated data, which is often unavailable. To address this lack, in this paper, we leverage, on one hand, massive amount of available unlabeled or weakly labeled data, and on the other hand, non-real images created through Generative Adversarial Networks. In particular, we propose a semi-supervised framework ,based on Generative Adversarial Networks (GANs), which consists of a generator network to provide extra training examples to a multi-class classifier, acting as discriminator in the GAN framework, that assigns sample a label y from the K possible classes or marks it as a fake sample (extra class). The underlying idea is that adding large fake visual data forces real samples to be close in the feature space, enabling a bottom-up clustering process, which, in turn, improves multiclass pixel classification. To ensure higher quality of generated images for GANs with consequent improved pixel classification, we extend the above framework by adding weakly annotated data, i.e., we provide class level information to the generator. We tested our approaches on several challenging benchmarking visual datasets, i.e. PASCAL, SiftFLow, Stanford and CamVid, achieving competitive performance also compared to state-of-the-art semantic segmentation method

Connected through hyperedge HE-1 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "on accurate evaluation of gans for language generation"
    - "neural text generation past present and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"

Connected through hyperedge HE-2 (induced by source paper "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "game theoretic analysis of road user safety scenarios involving autonomous vehicles"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-3 (induced by source paper "fast video object segmentation with spatio temporal gans"):
  with members:
    - "video to video synthesis"
    - "learning to adapt structured output space for semantic segmentation"
    - "unsupervised person image synthesis in arbitrary poses"
    - "fast and accurate online video object segmentation via tracking parts"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"

Connected through hyperedge HE-4 (induced by source paper "object segmentation using pixel wise adversarial loss"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "averaging weights leads to wider optima and better generalization"
    - "cycada cycle consistent adversarial domain adaptation"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation"):
  with members:
    - "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "map management for efficient long term visual localization in outdoor environments"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "night to day image translation for retrieval based localization"

Connected through hyperedge HE-6 (induced by source paper "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"):
  with members:
    - "amalgamating knowledge towards comprehensive classification"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "generalizing semi supervised generative adversarial networks to regression using feature contrasting"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "convergence problems with generative adversarial networks gans"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "virtual to real reinforcement learning for autonomous driving"
    - "good semi supervised learning that requires a bad gan"
    - "improved training of wasserstein gans"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "coarse to fine semantic segmentation from image level labels"):
  with members:
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "decoupled spatial neural attention for weakly supervised semantic segmentation"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

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)