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: "multi scale location aware kernel representation for object detection"
  Abstract: Although Faster R-CNN and its variants have shown promising performance in object detection, they only exploit simple first-order representation of object proposals for final classification and regression. Recent classification methods demonstrate that the integration of high-order statistics into deep convolutional neural networks can achieve impressive improvement, but their goal is to model whole images by discarding location information so that they cannot be directly adopted to object detection. In this paper, we make an attempt to exploit high-order statistics in object detection, aiming at generating more discriminative representations for proposals to enhance the performance of detectors. To this end, we propose a novel Multi-scale Location-aware Kernel Representation (MLKP) to capture high-order statistics of deep features in proposals. Our MLKP can be efficiently computed on a modified multi-scale feature map using a low-dimensional polynomial kernel approximation.Moreover, different from existing orderless global representations based on high-order statistics, our proposed MLKP is location retentive and sensitive so that it can be flexibly adopted to object detection. Through integrating into Faster R-CNN schema, the proposed MLKP achieves very competitive performance with state-of-the-art methods, and improves Faster R-CNN by 4.9% (mAP), 4.7% (mAP) and 5.0% (AP at IOU=[0.5:0.05:0.95]) on PASCAL VOC 2007, VOC 2012 and MS COCO benchmarks, respectively. Code is available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "towards accurate one stage object detection with ap loss"

Connected through hyperedge HE-2 (induced by source paper "a deep one shot network for query based logo retrieval"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "texture synthesis guided deep hashing for texture image retrieval"
    - "open logo detection challenge"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "scalable deep learning logo detection"
    - "focal loss for dense object detection"
    - "deep learning for logo recognition"

Connected through hyperedge HE-3 (induced by source paper "few shot learning for domain specfic fine grained image classfication"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "few shot learning with metric agnostic conditional embeddings"
    - "discriminative learning of latent features for zero shot recognition"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "git loss for deep face recognition"
    - "dynamic few shot visual learning without forgetting"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "distance iou loss faster and better learning for bounding box regression"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "object detection based on region decomposition and assembly"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "feature selective anchor free module for single shot object detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "fcos fully convolutional one stage object detection"
    - "data augmentation for object detection via progressive and selective instance switching"

Connected through hyperedge HE-5 (induced by source paper "rotated feature network for multi orientation object detection"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "edit probability for scene text recognition"
    - "focal loss for dense object detection"
    - "oriented response networks"
    - "squeeze and excitation networks"
    - "deep direct regression for multi oriented scene text detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (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"
    - "pseudo mask augmented object detection"
    - "revisiting rcnn on awakening the classification power of faster rcnn"

Connected through hyperedge HE-7 (induced by source paper "global second order pooling convolutional networks"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "dropblock a regularization method for convolutional networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"
    - "non linear convolution filters for cnn based learning"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "deep regionlets for object detection"):
  with members:
    - "cross domain visual recognition via domain adaptive dictionary learning"
    - "cornernet detecting objects as paired keypoints"
    - "pseudo mask augmented object detection"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "soft sampling for robust object detection"
    - "soft nms improving object detection with one line of code"
    - "deformable part based fully convolutional network for object detection"
    - "focal loss for dense 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)