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 detection via a multi region semantic segmentation aware cnn model"
  Abstract: We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "face detection with feature pyramids and landmarks"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "robust and high performance face detector"
    - "self driving cars a survey"
    - "refineface refinement neural network for high performance face detection"
    - "searching for mobilenetv3"
    - "pyramidbox high performance detector for finding tiny face"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "retinaface single stage dense face localisation in the wild"

Connected through hyperedge HE-2 (induced by source paper "pseudo mask augmented object detection"):
  with members:
    - "focal loss for dense object detection"
    - "ron reverse connection with objectness prior networks for object detection"
    - "mask r cnn"
    - "instance sensitive fully convolutional networks"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "cross stitch networks for multi task learning"):
  with members:
    - "fast r cnn"
    - "multi task cnn model for attribute prediction"
    - "how transferable are features in deep neural networks"
    - "simultaneous detection and segmentation"
    - "r cnns for pose estimation and action detection"
    - "imagenet large scale visual recognition challenge"
    - "designing deep networks for surface normal estimation"
    - "learning rich features from rgb d images for object detection and segmentation"

Connected through hyperedge HE-4 (induced by source paper "locnet improving localization accuracy for object detection"):
  with members:
    - "what makes for effective detection proposals"
    - "learning to segment object candidates"
    - "you only look once unified real time object detection"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "adaptive object detection using adjacency and zoom prediction"):
  with members:
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "efficient object detection for high resolution images"

Connected through hyperedge HE-6 (induced by source paper "window object relationship guided representation learning for generic object detections"):
  with members:
    - "attentionnet aggregating weak directions for accurate object detection"
    - "fast r cnn"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "generic object detection with dense neural patterns and regionlets"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "proposal free network for instance level object segmentation"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "learning to segment object candidates"
    - "fast r cnn"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "computational baby learning"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

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)