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: "dockerface an easy to install and use faster r cnn face detector in a docker container"
  Abstract: Face detection is a very important task and a necessary pre-processing step for many applications such as facial landmark detection, pose estimation, sentiment analysis and face recognition. Not only is face detection an important pre-processing step in computer vision applications but also in computational psychology, behavioral imaging and other fields where researchers might not be initiated in computer vision frameworks and state-of-the-art detection applications. A large part of existing research that includes face detection as a pre-processing step uses existing out-of-the-box detectors such as the HoG-based dlib and the OpenCV Haar face detector which are no longer state-of-the-art - they are primarily used because of their ease of use and accessibility. We introduce Dockerface, a very accurate Faster R-CNN face detector in a Docker container which requires no training and is easy to install and use.

Connected through hyperedge HE-1 (induced by source paper "social relation recognition in egocentric photostreams"):
  with members:
    - "person recognition in personal photo collections"
    - "batch based activity recognition from egocentric photo streams revisited"
    - "a domain based approach to social relation recognition"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "an overview of multi task learning in deep neural networks"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "fine grained head pose estimation without keypoints"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "faceposenet making a case for landmark free face alignment"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "stacked hourglass networks for human pose estimation"
    - "face detection with the faster r cnn"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "triplet probabilistic embedding for face verification and clustering"

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)