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: "end to end face detection and recognition"
  Abstract: Plenty of face detection and recognition methods have been proposed and got delightful results in decades. Common face recognition pipeline consists of: 1) face detection, 2) face alignment, 3) feature extraction, 4) similarity calculation, which are separated and independent from each other. The separated face analyzing stages lead the model redundant calculation and are hard for end-to-end training. In this paper, we proposed a novel end-to-end trainable convolutional network framework for face detection and recognition, in which a geometric transformation matrix was directly learned to align the faces, instead of predicting the facial landmarks. In training stage, our single CNN model is supervised only by face bounding boxes and personal identities, which are publicly available from WIDER FACE \cite{Yang2016} dataset and CASIA-WebFace \cite{Yi2014} dataset. Tested on Face Detection Dataset and Benchmark (FDDB) \cite{Jain2010} dataset and Labeled Face in the Wild (LFW) \cite{Huang2007} dataset, we have achieved 89.24\% recall for face detection task and 98.63\% verification accuracy for face recognition task simultaneously, which are comparable to state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "deployment of customized deep learning based video analytics on surveillance cameras"):
  with members:
    - "yolov3 an incremental improvement"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a unified multi faceted video summarization system"
    - "simple online and realtime tracking"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "embedded implementation of a deep learning smile detector"):
  with members:
    - "elastic neural networks a scalable framework for embedded computer vision"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "multimodal affect analysis for product feedback assessment"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "face detection with the faster r cnn"
    - "xception deep learning with depthwise separable convolutions"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"

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)