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: "spatial semantic regularisation for large scale object detection"
  Abstract: Large scale object detection with thousands of classes introduces the problem of many contradicting false positive detections, which have to be suppressed. Class-independent non-maximum suppression has traditionally been used for this step, but it does not scale well as the number of classes grows. Traditional non-maximum suppression does not consider label- and instance-level relationships nor does it allow an exploitation of the spatial layout of detection proposals. We propose a new multi-class spatial semantic regularisation method based on affinity propagation clustering, which simultaneously optimises across all categories and all proposed locations in the image, to improve both the localisation and categorisation of selected detection proposals. Constraints are shared across the labels through the semantic WordNet hierarchy. Our approach proves to be especially useful in large scale settings with thousands of classes, where spatial and semantic interactions are very frequent and only weakly supervised detectors can be built due to a lack of bounding box annotations. Detection experiments are conducted on the ImageNet and COCO dataset, and in settings with thousands of detected categories. Our method provides a significant precision improvement by reducing false positives, while simultaneously improving the recall.

Connected through hyperedge HE-1 (induced by source paper "precise detection in densely packed scenes"):
  with members:
    - "deep learning for generic object detection a survey"
    - "acquisition of localization confidence for accurate object detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "soft nms improving object detection with one line of code"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "learning non maximum suppression"

Connected through hyperedge HE-2 (induced by source paper "t cnn tubelets with convolutional neural networks for object detection from videos"):
  with members:
    - "cost effective active learning for deep image classification"
    - "object detection in videos with tubelet proposal networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "seq nms for video object detection"
    - "craft objects from images"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "attributes as semantic units between natural language and visual recognition"):
  with members:
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "segmentation from natural language expressions"
    - "image captioning and visual question answering based on attributes and their related external knowledge"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-4 (induced by source paper "learning non maximum suppression"):
  with members:
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "learning convolutional neural networks for graphs"
    - "end to end training of object class detectors for mean average precision"
    - "object localization in imagenet by looking out of the window"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "a convnet for non maximum suppression"

Connected through hyperedge HE-5 (induced by source paper "learning detection with diverse proposals"):
  with members:
    - "ssd single shot multibox detector"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "end to end integration of a convolutional network deformable parts model and non maximum suppression"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "soft nms improving object detection with one line of code"):
  with members:
    - "deformable convolutional networks"
    - "ssd single shot multibox detector"
    - "beyond skip connections top down modulation for object detection"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "caffe convolutional architecture for fast feature embedding"

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)