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: "multiview rgb d dataset for object instance detection"
  Abstract: This paper presents a new multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. Also, an approach for detection and recognition is presented, which is comprised of two parts: i) a new multi-view 3D proposal generation method and ii) the development of several recognition baselines using AlexNet to score our proposals, which is trained either on crops of the dataset or on synthetically composited training images. Finally, we compare the performance of the object proposals and a detection baseline to the Washington RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset is more challenging for object detection and recognition. The dataset is available 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"
    - "towards universal object detection by domain attention"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning to generate synthetic data via compositing"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "spatially transformed adversarial examples"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "loss rank mining a general hard example mining method for real time detectors"
    - "modeling visual context is key to augmenting object detection datasets"
    - "unsupervised hard example mining from videos for improved object detection"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "can 3d pose be learned from 2d projections alone"

Connected through hyperedge HE-3 (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-4 (induced by source paper "towards universal object detection by domain attention"):
  with members:
    - "yolov3 an incremental improvement"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "3d objectness estimation via bottom up regret grouping"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "object detection with deep learning a review"
    - "orthographic feature transform for monocular 3d object detection"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"

Connected through hyperedge HE-6 (induced by source paper "formal methods and software engineering for dl security safety and productivity for dl systems development"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "towards proving the adversarial robustness of deep neural networks"
    - "output range analysis for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "combining deep learning and verification for precise object instance detection"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "target driven instance detection"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "a dataset for developing and benchmarking active vision"
    - "synthesizing training data for object detection in indoor scenes"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-8 (induced by source paper "target driven instance detection"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "a dataset for developing and benchmarking active vision"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "synthesizing training data for object detection in indoor scenes"
    - "fully convolutional siamese networks for object 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)