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: "subspace alignment based domain adaptation for rcnn detector"
  Abstract: In this paper, we propose subspace alignment based domain adaptation of the state of the art RCNN based object detector. The aim is to be able to achieve high quality object detection in novel, real world target scenarios without requiring labels from the target domain. While, unsupervised domain adaptation has been studied in the case of object classification, for object detection it has been relatively unexplored. In subspace based domain adaptation for objects, we need access to source and target subspaces for the bounding box features. The absence of supervision (labels and bounding boxes are absent) makes the task challenging. In this paper, we show that we can still adapt sub- spaces that are localized to the object by obtaining detections from the RCNN detector trained on source and applied on target. Then we form localized subspaces from the detections and show that subspace alignment based adaptation between these subspaces yields improved object detection. This evaluation is done by considering challenging real world datasets of PASCAL VOC as source and validation set of Microsoft COCO dataset as target for various categories.

Connected through hyperedge HE-1 (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"
    - "a robust learning approach to domain adaptive object detection"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a robust learning approach to domain adaptive object detection"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "exploring object relation in mean teacher for cross domain detection"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "semantic foggy scene understanding with synthetic data"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "exploring visual relationship for image captioning"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-4 (induced by source paper "balancing domain gap for object instance detection"):
  with members:
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multimodal unsupervised image to image translation"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "autodial automatic domain alignment layers"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "few shot adaptive faster r cnn"):
  with members:
    - "few example object detection with model communication"
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "lstd a low shot transfer detector for object detection"
    - "focal loss for dense object detection"
    - "few shot adversarial domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"

Connected through hyperedge HE-6 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"

Connected through hyperedge HE-7 (induced by source paper "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"):
  with members:
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "towards universal object detection by domain attention"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "complement objective training"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "on learning invariant representation for domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "omnia faster r cnn detection in the wild through dataset merging and soft distillation"):
  with members:
    - "zero shot detection"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combining heterogeneously labeled datasets for training segmentation networks"
    - "reducing overconfident errors outside the known distribution"
    - "open set domain adaptation by backpropagation"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"

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)