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: "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"
  Abstract: A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity documents recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems. Since an important feature of identity documents is their sensitiveness as they contain personal data, all source document images used in MIDV-500 are either in public domain or distributed under public copyright licenses. The dataset is available for download at this ftp URL.

Connected through hyperedge HE-1 (induced by source paper "face detection in camera captured images of identity documents under challenging conditions"):
  with members:
    - "real time rotation invariant face detection with progressive calibration networks"
    - "docface matching id document photos to selfies"
    - "ssh single stage headless face detector"
    - "grid loss detecting occluded faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "wider face a face detection benchmark"
    - "from facial parts responses to face detection a deep learning approach"

Connected through hyperedge HE-2 (induced by source paper "identity document and banknote security forensics a survey"):
  with members:
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "noiseprint a cnn based camera model fingerprint"
    - "camera based image forgery localization using convolutional neural networks"
    - "e counterfeit a mobile server platform for document counterfeit detection"
    - "humans are easily fooled by digital images"
    - "machine assisted authentication of paper currency an experiment on indian banknotes"

Connected through hyperedge HE-3 (induced by source paper "houghnet neural network architecture for vanishing points detection"):
  with members:
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "robust scene text recognition with automatic rectification"

Connected through hyperedge HE-4 (induced by source paper "next integrated result modelling for stopping the text field recognition process in a video using a result model with per character alternatives"):
  with members:
    - "optical font recognition in smartphone captured images and its applicability for id forgery detection"
    - "dynamic programming approach to template based ocr"

Connected through hyperedge HE-5 (induced by source paper "methods of weighted combination for text field recognition in a video stream"):
  with members:
    - "efficient video scene text spotting unifying detection tracking and recognition"
    - "a survey on optical character recognition system"

Connected through hyperedge HE-6 (induced by source paper "fast projective image rectification for planar objects with manhattan structure"):
  with members:
    - "houghnet neural network architecture for vanishing points detection"

Connected through hyperedge HE-7 (induced by source paper "midv 2019 challenges of the modern mobile based document ocr"):
  with members:
    - "design of an optical character recognition system for camera based handheld devices"

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)