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: "object proposal evaluation protocol is gameable"
  Abstract: Object proposals have quickly become the de-facto pre-processing step in a number of vision pipelines (for object detection, object discovery, and other tasks). Their performance is usually evaluated on partially annotated datasets. In this paper, we argue that the choice of using a partially annotated dataset for evaluation of object proposals is problematic -- as we demonstrate via a thought experiment, the evaluation protocol is 'gameable', in the sense that progress under this protocol does not necessarily correspond to a "better" category independent object proposal algorithm. #R##N#To alleviate this problem, we: (1) Introduce a nearly-fully annotated version of PASCAL VOC dataset, which serves as a test-bed to check if object proposal techniques are overfitting to a particular list of categories. (2) Perform an exhaustive evaluation of object proposal methods on our introduced nearly-fully annotated PASCAL dataset and perform cross-dataset generalization experiments; and (3) Introduce a diagnostic experiment to detect the bias capacity in an object proposal algorithm. This tool circumvents the need to collect a densely annotated dataset, which can be expensive and cumbersome to collect. Finally, we plan to release an easy-to-use toolbox which combines various publicly available implementations of object proposal algorithms which standardizes the proposal generation and evaluation so that new methods can be added and evaluated on different datasets. We hope that the results presented in the paper will motivate the community to test the category independence of various object proposal methods by carefully choosing the evaluation protocol.

Connected through hyperedge HE-1 (induced by source paper "cascade rpn delving into high quality region proposal network with adaptive convolution"):
  with members:
    - "region proposal by guided anchoring"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "deepqoe a unified framework for learning to predict video qoe"
    - "instance level salient object segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "quasi homography warps in image stitching"
    - "instance sensitive fully convolutional networks"
    - "convolutional oriented boundaries"
    - "salient object subitizing"

Connected through hyperedge HE-3 (induced by source paper "learning objectness from sonar images for class independent object detection"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "real time convolutional networks for sonar image classification in low power embedded systems"
    - "what makes for effective detection proposals"
    - "objectness scoring and detection proposals in forward looking sonar images with convolutional neural networks"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "how good are detection proposals really"

Connected through hyperedge HE-4 (induced by source paper "toward scale invariance and position sensitive region proposal networks"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "training region based object detectors with online hard example mining"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "feature pyramid networks for object detection"
    - "r fcn object detection via region based fully convolutional networks"
    - "learning to segment object candidates"

Connected through hyperedge HE-5 (induced by source paper "mason a model agnostic objectness framework"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "zoom out and in network with map attention decision for region proposal and object detection"):
  with members:
    - "learning deep features via congenerous cosine loss for person recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "recurrent scale approximation for object detection in cnn"
    - "stacked hourglass networks for human pose estimation"
    - "deepproposals hunting objects and actions by cascading deep convolutional layers"
    - "scale aware pixelwise object proposal networks"

Connected through hyperedge HE-7 (induced by source paper "zoom out and in network with recursive training for object proposal"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "deepproposals hunting objects and actions by cascading deep convolutional layers"
    - "ssd single shot multibox detector"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "what makes for effective detection proposals"
    - "multi bias non linear activation in deep neural networks"
    - "craft objects from images"
    - "learning to segment object candidates"

Connected through hyperedge HE-8 (induced by source paper "attend refine repeat active box proposal generation via in out localization"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "training region based object detectors with online hard example mining"
    - "what makes for effective detection proposals"
    - "a multipath network for object detection"
    - "learning to segment object candidates"
    - "instance aware semantic segmentation via multi task network cascades"
    - "locnet improving localization accuracy for object detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"

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)