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: "a robust learning approach to domain adaptive object detection"
  Abstract: Domain shift is unavoidable in real-world applications of object detection. For example, in self-driving cars, the target domain consists of unconstrained road environments which cannot all possibly be observed in training data. Similarly, in surveillance applications sufficiently representative training data may be lacking due to privacy regulations. In this paper, we address the domain adaptation problem from the perspective of robust learning and show that the problem may be formulated as training with noisy labels. We propose a robust object detection framework that is resilient to noise in bounding box class labels, locations and size annotations. To adapt to the domain shift, the model is trained on the target domain using a set of noisy object bounding boxes that are obtained by a detection model trained only in the source domain. We evaluate the accuracy of our approach in various source/target domain pairs and demonstrate that the model significantly improves the state-of-the-art on multiple domain adaptation scenarios on the SIM10K, Cityscapes and KITTI datasets.

Connected through hyperedge HE-1 (induced by source paper "unbiased mean teacher for cross domain object detection"):
  with members:
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "automatic adaptation of object detectors to new domains using self training"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "wasserstein distance based domain adaptation for object detection"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-2 (induced by source paper "deep domain adaptive object detection a survey"):
  with members:
    - "ifan image instance full alignment networks for adaptive object detection"
    - "few shot adaptive faster r cnn"
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"
    - "borrow from anywhere pseudo multi modal object detection in thermal imagery"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "progressive domain adaptation for object detection"

Connected through hyperedge HE-3 (induced by source paper "reveal of domain effect how visual restoration contributes to object detection in aquatic scenes"):
  with members:
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "bottom up object detection by grouping extreme and center points"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "selective refinement network for high performance face detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-4 (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"
    - "confidence regularized self training"
    - "multi adversarial faster rcnn for unrestricted 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-5 (induced by source paper "prior based domain adaptive object detection for adverse weather conditions"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "single image deraining a comprehensive benchmark analysis"
    - "automatic adaptation of object detectors to new domains using self training"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "vision meets drones a challenge"
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "density aware single image de raining using a multi stream dense network"

Connected through hyperedge HE-6 (induced by source paper "instance invariant adaptive object detection via progressive disentanglement"):
  with members:
    - "theory and evaluation metrics for learning disentangled representations"
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "domain agnostic learning with disentangled representations"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "cascade rpn delving into high quality region proposal network with adaptive convolution"
    - "exploring object relation in mean teacher for cross domain detection"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"

Connected through hyperedge HE-7 (induced by source paper "it gan do better gan based detection of objects on images with varying quality"):
  with members:
    - "deepcorrect correcting dnn models against image distortions"
    - "focal loss for dense object detection"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "perceptual generative adversarial networks for small object detection"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "examining the impact of blur on recognition by convolutional networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "how image degradations affect deep cnn based face recognition"

Connected through hyperedge HE-8 (induced by source paper "curriculum self paced learning for cross domain object detection"):
  with members:
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "beyond sharing weights for deep domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised visual representation learning by context prediction"
    - "deep residual learning for 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.LG(Machine Learning)