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: "adaptive semantic segmentation with a strategic curriculum of proxy labels"
  Abstract: Training deep networks for semantic segmentation requires annotation of large amounts of data, which can be time-consuming and expensive. Unfortunately, these trained networks still generalize poorly when tested in domains not consistent with the training data. In this paper, we show that by carefully presenting a mixture of labeled source domain and proxy-labeled target domain data to a network, we can achieve state-of-the-art unsupervised domain adaptation results. With our design, the network progressively learns features specific to the target domain using annotation from only the source domain. We generate proxy labels for the target domain using the network's own predictions. Our architecture then allows selective mining of easy samples from this set of proxy labels, and hard samples from the annotated source domain. We conduct a series of experiments with the GTA5, Cityscapes and BDD100k datasets on synthetic-to-real domain adaptation and geographic domain adaptation, showing the advantages of our method over baselines and existing approaches.

Connected through hyperedge HE-1 (induced by source paper "regularizing proxies with multi adversarial training for unsupervised domain adaptive semantic segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"):
  with members:
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "self paced deep learning for weakly supervised object detection"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "zigzag learning for weakly supervised object detection"
    - "open set domain adaptation by backpropagation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "strong weak distribution alignment for adaptive object detection"

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"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-4 (induced by source paper "less is more an exploration of data redundancy with active dataset subsampling"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "are all training examples created equal an empirical study"
    - "practical obstacles to deploying active learning"
    - "an empirical study of example forgetting during deep neural network learning"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-5 (induced by source paper "large scale visual active learning with deep probabilistic ensembles"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "model based active exploration"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "on calibration of modern neural networks"
    - "active one shot learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

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)