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: "confidence regularized self training"
  Abstract: Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation. These methods often involve an iterative process of predicting on target domain and then taking the confident predictions as pseudo-labels for retraining. However, since pseudo-labels can be noisy, self-training can put overconfident label belief on wrong classes, leading to deviated solutions with propagated errors. To address the problem, we propose a confidence regularized self-training (CRST) framework, formulated as regularized self-training. Our method treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization. We propose two types of confidence regularization: label regularization (LR) and model regularization (MR). CRST-LR generates soft pseudo-labels while CRST-MR encourages the smoothness on network output. Extensive experiments on image classification and semantic segmentation show that CRSTs outperform their non-regularized counterpart with state-of-the-art performance. The code and models of this work are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels"):
  with members:
    - "universal domain adaptation through self supervision"
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "unsupervised deep learning by neighbourhood discovery"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-2 (induced by source paper "low budget unsupervised label query through domain alignment enforcement"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "beyond sharing weights for deep domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "self paced deep learning for weakly supervised object detection"
    - "unsupervised domain adaptation through self supervision"

Connected through hyperedge HE-3 (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"
    - "an adversarial perturbation oriented domain adaptation approach for semantic segmentation"
    - "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-4 (induced by source paper "realizing pixel level semantic learning in complex driving scenes based on only one annotated pixel per class"):
  with members:
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "weakly and semi supervised panoptic segmentation"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-5 (induced by source paper "understanding self training for gradual domain adaptation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "understanding and mitigating the tradeoff between robustness and accuracy"
    - "are labels required for improving adversarial robustness"
    - "self training with noisy student improves imagenet classification"
    - "on learning invariant representation for domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "adversarial domain adaptation for stable brain machine interfaces"
    - "cross domain weakly supervised object detection through progressive domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "less confusion more transferable minimum class confusion for versatile domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "domain agnostic learning with disentangled representations"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "on learning invariant representation for domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "learning from synthetic animals"):
  with members:
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "a robust learning approach to domain adaptive object detection"
    - "nlnl negative learning for noisy labels"
    - "automatic adaptation of object detectors to new domains using self training"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "amur tiger re identification in the wild"

Connected through hyperedge HE-8 (induced by source paper "angular visual hardness"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "decoupled networks"
    - "reducing overconfident errors outside the known distribution"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning towards minimum hyperspherical energy"
    - "not all samples are created equal deep learning with importance sampling"

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)