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 with pixel intensity comparisons organized in decision trees"
  Abstract: We describe a method for visual object detection based on an ensemble of optimized decision trees organized in a cascade of rejectors. The trees use pixel intensity comparisons in their internal nodes and this makes them able to process image regions very fast. Experimental analysis is provided through a face detection problem. The obtained results are encouraging and demonstrate that the method has practical value. Additionally, we analyse its sensitivity to noise and show how to perform fast rotation invariant object detection. Complete source code is provided at https://github.com/nenadmarkus/pico.

Connected through hyperedge HE-1 (induced by source paper "survey of face detection on low quality images"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "s 3 fd single shot scale invariant face detector"
    - "detecting faces using region based fully convolutional networks"
    - "face detection through scale friendly deep convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-2 (induced by source paper "cms rcnn contextual multi scale region based cnn for unconstrained face detection"):
  with members:
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "a fast and accurate unconstrained face detector"
    - "a multipath network for object detection"
    - "multi view face detection using deep convolutional neural networks"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "parsenet looking wider to see better"
    - "occlusion coherence detecting and localizing occluded faces"
    - "fast r cnn"

Connected through hyperedge HE-3 (induced by source paper "can you find a face in a hevc bitstream"):
  with members:
    - "can you tell a face from a hevc bitstream"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "from facial parts responses to face detection a deep learning approach"

Connected through hyperedge HE-4 (induced by source paper "object specific deep learning feature and its application to face detection"):
  with members:
    - "understanding neural networks through deep visualization"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "multi view face detection using deep convolutional neural networks"
    - "from facial parts responses to face detection a deep learning approach"
    - "convolutional channel features"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"

Connected through hyperedge HE-5 (induced by source paper "compact convolutional neural network cascade for face detection"):
  with members:
    - "a fast and accurate unconstrained face detector"
    - "face detection with a 3d model"
    - "multi view face detection using deep convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "from facial parts responses to face detection a deep learning approach"
    - "going deeper with convolutions"
    - "densenet implementing efficient convnet descriptor pyramids"
    - "fast convolutional nets with fbfft a gpu performance evaluation"

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)