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: "locnet improving localization accuracy for object detection"
  Abstract: We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems. Our model, given a search region, aims at returning the bounding box of an object of interest inside this region. To accomplish its goal, it relies on assigning conditional probabilities to each row and column of this region, where these probabilities provide useful information regarding the location of the boundaries of the object inside the search region and allow the accurate inference of the object bounding box under a simple probabilistic framework. #R##N#For implementing our localization model, we make use of a convolutional neural network architecture that is properly adapted for this task, called LocNet. We show experimentally that LocNet achieves a very significant improvement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and that it can be very easily coupled with recent state-of-the-art object detection systems, helping them to boost their performance. Finally, we demonstrate that our detection approach can achieve high detection accuracy even when it is given as input a set of sliding windows, thus proving that it is independent of box proposal methods.

Connected through hyperedge HE-1 (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-2 (induced by source paper "side aware boundary localization for more precise object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "switchable whitening for deep representation learning"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "nas fcos fast neural architecture search for object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-3 (induced by source paper "deep fitting degree scoring network for monocular 3d object detection"):
  with members:
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "3d object proposals using stereo imagery for accurate object class detection"
    - "3d bounding box estimation using deep learning and geometry"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-4 (induced by source paper "deep regionlets blended representation and deep learning for generic object detection"):
  with members:
    - "an analysis of pre training on object detection"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "cornernet detecting objects as paired keypoints"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "deep feature pyramid reconfiguration for object detection"
    - "multi scale location aware kernel representation for object detection"
    - "pseudo mask augmented object detection"

Connected through hyperedge HE-5 (induced by source paper "fa rpn floating region proposals for face detection"):
  with members:
    - "dsfd dual shot face detector"
    - "autofocus efficient multi scale inference"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"
    - "sniper efficient multi scale training"
    - "soft nms improving object detection with one line of code"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"

Connected through hyperedge HE-6 (induced by source paper "bsn boundary sensitive network for temporal action proposal generation"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "activitynet challenge 2017 summary"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-7 (induced by source paper "sniper efficient multi scale training"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"
    - "deformable convolutional networks"
    - "megdet a large mini batch object detector"
    - "mixed precision training"
    - "an analysis of scale invariance in object detection snip"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-8 (induced by source paper "grid r cnn"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "single shot refinement neural network for object detection"
    - "megdet a large mini batch object detector"
    - "an analysis of scale invariance in object detection snip"

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)