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 multipath network for object detection"
  Abstract: The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization. The result of these modifications is that information can flow along multiple paths in our network, including through features from multiple network layers and from multiple object views. We refer to our modified classifier as a "MultiPath" network. We couple our MultiPath network with DeepMask object proposals, which are well suited for localization and small objects, and adapt our pipeline to predict segmentation masks in addition to bounding boxes. The combined system improves results over the baseline Fast R-CNN detector with Selective Search by 66% overall and by 4x on small objects. It placed second in both the COCO 2015 detection and segmentation challenges.

Connected through hyperedge HE-1 (induced by source paper "har net joint learning of hybrid attention for single stage object detection"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "feature selective anchor free module for single shot object detection"
    - "sharp attention network via adaptive sampling for person re identification"
    - "group normalization"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "cascade r cnn high quality object detection and instance segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-3 (induced by source paper "segmenting the future"):
  with members:
    - "predicting future instance segmentations by forecasting convolutional features"
    - "action agnostic human pose forecasting"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "context encoding for semantic segmentation"
    - "d3d distilled 3d networks for video action recognition"
    - "future semantic segmentation with convolutional lstm"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-4 (induced by source paper "sinet a scale insensitive convolutional neural network for fast vehicle detection"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "a joint convolutional neural networks and context transfer for street scenes labeling"
    - "3d bounding box estimation using deep learning and geometry"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "aggregated residual transformations for deep neural networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "tensormask a foundation for dense object segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "rethinking imagenet pre training"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "megdet a large mini batch object detector"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "delving deep into liver focal lesion detection a preliminary study"):
  with members:
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "ssd single shot multibox detector"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "learning to refine object segments"
    - "feature pyramid networks for object detection"
    - "3d deeply supervised network for automatic liver segmentation from ct volumes"

Connected through hyperedge HE-7 (induced by source paper "recurrent existence determination through policy optimization"):
  with members:
    - "salient object detection a survey"
    - "metatrace actor critic online step size tuning by meta gradient descent for reinforcement learning control"
    - "policy optimization with second order advantage information"
    - "asynchronous methods for deep reinforcement learning"
    - "attend infer repeat fast scene understanding with generative models"
    - "end to end learning of action detection from frame glimpses in videos"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-8 (induced by source paper "path aggregation network for instance segmentation"):
  with members:
    - "focal loss for dense object detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "mask r cnn"

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)