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: "an adversarial perturbation oriented domain adaptation approach for semantic segmentation"
  Abstract: We focus on Unsupervised Domain Adaptation (UDA) for the task of semantic segmentation. Recently, adversarial alignment has been widely adopted to match the marginal distribution of feature representations across two domains globally. However, this strategy fails in adapting the representations of the tail classes or small objects for semantic segmentation since the alignment objective is dominated by head categories or large objects. In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations. Specifically, we firstly perturb the intermediate feature maps with several attack objectives (i.e., discriminator and classifier) on each individual position for both domains, and then the classifier is trained to be invariant to the perturbations. By perturbing each position individually, our model treats each location evenly regardless of the category or object size and thus circumvents the aforementioned issue. Moreover, the domain gap in feature space is reduced by extrapolating source and target perturbed features towards each other with attack on the domain discriminator. Our approach achieves the state-of-the-art performance on two challenging domain adaptation tasks for semantic segmentation: GTA5 -> Cityscapes and SYNTHIA -> Cityscapes.

Connected through hyperedge HE-1 (induced by source paper "rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation"):
  with members:
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "dual graph convolutional network for semantic segmentation"
    - "unsupervised scene adaptation with memory regularization in vivo"
    - "confidence regularized self training"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "domain adaptation for structured output via discriminative patch representations"
    - "transferrable prototypical networks for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "heatnet bridging the day night domain gap in semantic segmentation with thermal images"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pst900 rgb thermal calibration dataset and segmentation network"
    - "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
    - "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "rgb t object tracking benchmark and baseline"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-3 (induced by source paper "unsupervised scene adaptation with memory regularization in vivo"):
  with members:
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "dual graph convolutional network for semantic segmentation"
    - "confidence regularized self training"
    - "domain adaptation for structured output via discriminative patch representations"
    - "regularizing proxies with multi adversarial training for unsupervised domain adaptive semantic segmentation"
    - "significance aware information bottleneck for domain adaptive semantic segmentation"
    - "manifold criterion guided transfer learning via intermediate domain generation"
    - "ace adapting to changing environments for semantic 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)