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: "compact convolutional neural network cascade for face detection"
  Abstract: The problem of faces detection in images or video streams is a classical problem of computer vision. The multiple solutions of this problem have been proposed, but the question of their optimality is still open. Many algorithms achieve a high quality face detection, but at the cost of high computational complexity. This restricts their application in the real-time systems. This paper presents a new solution of the frontal face detection problem based on compact convolutional neural networks cascade. The test results on FDDB dataset show that it is competitive with state-of-the-art algorithms. This proposed detector is implemented using three technologies: SSE/AVX/AVX2 instruction sets for Intel CPUs, Nvidia CUDA, OpenCL. The detection speed of our approach considerably exceeds all the existing CPU-based and GPU-based algorithms. Because of high computational efficiency, our detector can processing 4K Ultra HD video stream in real time (up to 27 fps) on mobile platforms (Intel Ivy Bridge CPUs and Nvidia Kepler GPUs) in searching objects with the dimension 60x60 pixels or higher. At the same time its performance weakly dependent on the background and number of objects in scene. This is achieved by the asynchronous computation of stages in the cascade.

Connected through hyperedge HE-1 (induced by source paper "a driver fatigue recognition algorithm based on spatio temporal feature sequence"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unitbox an advanced object detection network"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "face alignment assisted by head pose estimation"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "a vision based system for monitoring the loss of attention in automotive drivers"
    - "network in network"

Connected through hyperedge HE-2 (induced by source paper "fast and accurate object detection in high resolution 4k and 8k video using gpus"):
  with members:
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "towards automated tuberculosis detection using deep learning"):
  with members:
    - "detecting cancer metastases on gigapixel pathology images"

Connected through hyperedge HE-4 (induced by source paper "multi stage neural networks with single sided classifiers for false positive reduction and its evaluation using lung x ray ct images"):
  with members:
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "lung nodule classification by the combination of fusion classifier and cascaded convolutional neural networks"):
  with members:
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "a scalable convolutional neural network for task specified scenarios via knowledge distillation"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"
    - "learning both weights and connections for efficient neural networks"
    - "fast convnets using group wise brain damage"
    - "deep residual learning for image recognition"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"

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)