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: "scale aware pixelwise object proposal networks"
  Abstract: Object proposal is essential for current state-of-the-art object detection pipelines. However, the existing proposal methods generally fail in producing results with satisfying localization accuracy. The case is even worse for small objects, which, however, are quite common in practice. In this paper, we propose a novel scale-aware pixelwise object proposal network (SPOP-net) to tackle the challenges. The SPOP-net can generate proposals with high recall rate and average best overlap, even for small objects. In particular, in order to improve the localization accuracy, a fully convolutional network is employed which predicts locations of object proposals for each pixel. The produced ensemble of pixelwise object proposals enhances the chance of hitting the object significantly without incurring heavy extra computational cost. To solve the challenge of localizing objects at small scale, two localization networks, which are specialized for localizing objects with different scales are introduced, following the divide-and-conquer philosophy. Location outputs of these two networks are then adaptively combined to generate the final proposals by a large-/small-size weighting network. Extensive evaluations on PASCAL VOC 2007 and COCO 2014 show the SPOP network is superior over the state-of-the-art models. The high-quality proposals from SPOP-net also significantly improve the mean average precision of object detection with Fast-Regions with CNN features framework. Finally, the SPOP-net (trained on PASCAL VOC) shows great generalization performance when testing it on ILSVRC 2013 validation set.

Connected through hyperedge HE-1 (induced by source paper "region proposal by guided anchoring"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "metaanchor learning to detect objects with customized anchors"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "toward scale invariance and position sensitive region proposal networks"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-2 (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"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "scalenet guiding object proposal generation in supermarkets and beyond"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "researchdoom and cocodoom learning computer vision with games"
    - "instance sensitive fully convolutional networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "ssd single shot multibox detector"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-4 (induced by source paper "learning multilayer channel features for pedestrian detection"):
  with members:
    - "cascade learning by optimally partitioning"
    - "learning sampling distributions for efficient object detection"
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "how far are we from solving pedestrian detection"
    - "convolution in convolution for network in network"
    - "scale aware fast r cnn for pedestrian detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "filtered channel features for pedestrian detection"

Connected through hyperedge HE-5 (induced by source paper "unitbox an advanced object detection network"):
  with members:
    - "studying very low resolution recognition using deep networks"
    - "scale aware fast r cnn for pedestrian detection"
    - "deep residual learning for image recognition"
    - "densebox unifying landmark localization with end to end object detection"
    - "wider face a face detection benchmark"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "taking a deeper look at pedestrians"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "sequential optimization for efficient high quality object proposal generation"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "what makes for effective detection proposals"
    - "group membership prediction"
    - "fast r cnn"
    - "deepproposal hunting objects by cascading deep convolutional layers"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "very deep convolutional networks for large scale image recognition"

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)