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: "stuffnet using stuff to improve object detection"
  Abstract: We propose a Convolutional Neural Network (CNN) based algorithm - StuffNet - for object detection. In addition to the standard convolutional features trained for region proposal and object detection [31], StuffNet uses convolutional features trained for segmentation of objects and 'stuff' (amorphous categories such as ground and water). Through experiments on Pascal VOC 2010, we show the importance of features learnt from stuff segmentation for improving object detection performance. StuffNet improves performance from 18.8% mAP to 23.9% mAP for small objects. We also devise a method to train StuffNet on datasets that do not have stuff segmentation labels. Through experiments on Pascal VOC 2007 and 2012, we demonstrate the effectiveness of this method and show that StuffNet also significantly improves object detection performance on such datasets.

Connected through hyperedge HE-1 (induced by source paper "thinking outside the box generation of unconstrained 3d room layouts"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "weakly supervised object localization using things and stuff transfer"
    - "roomnet end to end room layout estimation"
    - "surface normals in the wild"
    - "mask r cnn"
    - "deeper depth prediction with fully convolutional residual networks"
    - "pano2cad room layout from a single panorama image"

Connected through hyperedge HE-2 (induced by source paper "driving scene perception network real time joint detection depth estimation and semantic segmentation"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "xception deep learning with depthwise separable convolutions"
    - "pyramid scene parsing network"

Connected through hyperedge HE-3 (induced by source paper "deep scene text detection with connected component proposals"):
  with members:
    - "cascaded segmentation detection networks for word level text spotting"
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"
    - "textboxes a fast text detector with a single deep neural network"
    - "detecting text in natural image with connectionist text proposal network"
    - "deeptext a unified framework for text proposal generation and text detection in natural images"
    - "spatial transformer networks"
    - "parsenet looking wider to see better"

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)