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: "adaptive object detection using adjacency and zoom prediction"
  Abstract: State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.

Connected through hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "f brs rethinking backpropagating refinement for interactive segmentation"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "interactive full image segmentation by considering all regions jointly"
    - "bag of tricks for image classification with convolutional neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dynamic zoom in network for fast object detection in large images"
    - "deep interactive object selection"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning"):
  with members:
    - "panoptic segmentation"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-4 (induced by source paper "efficient object detection in large images using deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "using pre training can improve model robustness and uncertainty"
    - "learning to interpret satellite images using wikipedia"
    - "xview objects in context in overhead imagery"
    - "autofocus efficient multi scale inference"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "clustered object detection in aerial images"):
  with members:
    - "high resolution representations for labeling pixels and regions"
    - "vision meets drones a challenge"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "learning roi transformer for detecting oriented objects in aerial images"
    - "focal loss for dense object detection"
    - "dota a large scale dataset for object detection in aerial images"
    - "cascade r cnn delving into high quality object detection"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-6 (induced by source paper "autofocus efficient multi scale inference"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "optimizing video object detection via a scale time lattice"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "soft nms improving object detection with one line of code"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"

Connected through hyperedge HE-7 (induced by source paper "efficient video understanding via layered multi frame rate analysis"):
  with members:
    - "object detection in video with spatiotemporal sampling networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "boosting domain adaptation by discovering latent domains"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "efficient neural architecture search via parameter sharing"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "ban focusing on boundary context for object detection"):
  with members:
    - "focal loss for dense object detection"
    - "deformable convolutional networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"

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)