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: "cell detection with deep convolutional neural network and compressed sensing"
  Abstract: The ability to automatically detect certain types of cells or cellular subunits in microscopy images is of significant interest to a wide range of biomedical research and clinical practices. Cell detection methods have evolved from employing hand-crafted features to deep learning-based techniques to locate target cells. The essential idea of these methods is that their cell classifiers or detectors are trained in the pixel space, where the locations of target cells are labeled. In this paper, we seek a different route and propose a convolutional neural network (CNN)-based cell detection method that uses encoding of the output pixel space. For the cell detection problem, the output space is the sparsely labeled pixel locations indicating cell centers. Consequently, we employ random projections to encode the output space to a compressed vector of fixed dimension. Then, CNN regresses this compressed vector from the input pixels. Using $L_1$-norm optimization, we recover sparse cell locations on the output pixel space from the predicted compressed vector. In the past, output space encoding using compressed sensing (CS) has been used in conjunction with linear and non-linear predictors. To the best of our knowledge, this is the first successful use of CNN with CS-based output space encoding. We experimentally demonstrate that proposed CNN + CS framework (referred to as CNNCS) exceeds the accuracy of the state-of-the-art methods on many benchmark datasets for microscopy cell detection. Additionally, we show that CNNCS can exploit ensemble average by using more than one random encodings of the output space. In the AMIDA13 MICCAI grand competition, we achieve the 3rd highest F1-score in all the 17 participated teams. More ranking details are available at this http URL Implementation of CNNCS is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "multi stream faster rcnn for mitosis counting in breast cancer images"):
  with members:
    - "bilinear faster rcnn with ela for image tampering detection"
    - "learning rich features for image manipulation detection"
    - "mitos rcnn a novel approach to mitotic figure detection in breast cancer histopathology images using region based convolutional neural networks"
    - "machine learning methods for histopathological image analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "compact bilinear pooling"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"

Connected through hyperedge HE-2 (induced by source paper "enhanced center coding for cell detection with convolutional neural networks"):
  with members:
    - "count ception counting by fully convolutional redundant counting"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "deep watershed transform for instance segmentation"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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)