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: "towards universal object detection by domain attention"
  Abstract: Despite increasing efforts on universal representations for visual recognition, few have addressed object detection. In this paper, we develop an effective and efficient universal object detection system that is capable of working on various image domains, from human faces and traffic signs to medical CT images. Unlike multi-domain models, this universal model does not require prior knowledge of the domain of interest. This is achieved by the introduction of a new family of adaptation layers, based on the principles of squeeze and excitation, and a new domain-attention mechanism. In the proposed universal detector, all parameters and computations are shared across domains, and a single network processes all domains all the time. Experiments, on a newly established universal object detection benchmark of 11 diverse datasets, show that the proposed detector outperforms a bank of individual detectors, a multi-domain detector, and a baseline universal detector, with a 1.3x parameter increase over a single-domain baseline detector. The code and benchmark will be released at this http URL.

Connected through hyperedge HE-1 (induced by source paper "continual universal object detection"):
  with members:
    - "distilling object detectors with fine grained feature imitation"
    - "continual lifelong learning with neural networks a review"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "learning without memorizing"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-2 (induced by source paper "self guided adaptation progressive representation alignment for domain adaptive object detection"):
  with members:
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "progressive domain adaptation for object detection"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking and vehicle re id in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "ram a region aware deep model for vehicle re identification"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "towards universal representation learning for deep face recognition"):
  with members:
    - "probabilistic face embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "additive margin softmax for face verification"
    - "on calibration of modern neural networks"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-5 (induced by source paper "dianet dense and implicit attention network"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "bam bottleneck attention module"
    - "mixed link networks"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "domain adaptive attention model for unsupervised cross domain person re identification"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "do imagenet classifiers generalize to imagenet"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"

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"
    - "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"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-8 (induced by source paper "visual privacy protection via mapping distortion"):
  with members:
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "feature selective anchor free module for single shot object detection"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "arcface additive angular margin loss for deep face recognition"
    - "pairwise confusion for fine grained visual classification"
    - "siamese instance search for tracking"
    - "ssd single shot multibox detector"

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)