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: "deepid net deformable deep convolutional neural networks for object detection"
  Abstract: In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN \cite{girshick2014rich}, which was the state-of-the-art, from 31\% to 50.3\% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1\%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provide a global view for people to understand the deep learning object detection pipeline.

Connected through hyperedge HE-1 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-2 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "video person re identification by temporal residual learning"
    - "person re identification with metric learning using privileged information"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning view specific deep networks for person re identification"

Connected through hyperedge HE-3 (induced by source paper "c rpns promoting object detection in real world via a cascade structure of region proposal networks"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "cdpm convolutional deformable part models for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "attention aware compositional network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "alignedreid surpassing human level performance in person re identification"
    - "end to end comparative attention networks for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "pedestrian alignment network for large scale person re identification"

Connected through hyperedge HE-5 (induced by source paper "efficient coarse to fine non local module for the detection of small objects"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "ocnet object context network for scene parsing"
    - "yolov3 an incremental improvement"
    - "compact generalized non local network"
    - "auto context r cnn"
    - "relational inductive biases deep learning and graph networks"
    - "non local recurrent network for image restoration"
    - "couplenet coupling global structure with local parts for object detection"

Connected through hyperedge HE-6 (induced by source paper "quantization mimic towards very tiny cnn for object detection"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "interleaved group convolutions for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "recurrent scale approximation for object detection in cnn"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "channel pruning for accelerating very deep neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "auto context r cnn"):
  with members:
    - "adversarial patch"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "non local neural networks"
    - "deformable convolutional networks"
    - "megdet a large mini batch object detector"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial examples that fool detectors"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-8 (induced by source paper "bi directional graph structure information model for multi person pose estimation"):
  with members:
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "object detection from video tubelets with convolutional neural networks"
    - "structured feature learning for pose estimation"

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)