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: "the via annotation software for images audio and video"
  Abstract: In this paper, we introduce a simple and standalone manual annotation tool for images, audio and video: the VGG Image Annotator (VIA). This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. The VIA software allows human annotators to define and describe spatial regions in images or video frames, and temporal segments in audio or video. These manual annotations can be exported to plain text data formats such as JSON and CSV and therefore are amenable to further processing by other software tools. VIA also supports collaborative annotation of a large dataset by a group of human annotators. The BSD open source license of this software allows it to be used in any academic project or commercial application.

Connected through hyperedge HE-1 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers"):
  with members:
    - "using word embeddings to examine gender bias in dutch newspapers 1950 1990"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "automatic detection and counting of retina cell nuclei using deep learning"):
  with members:
    - "an overview of deep learning in medical imaging focusing on mri"
    - "bounding box regression with uncertainty for accurate object detection"
    - "cell detection in microscopy images with deep convolutional neural network and compressed sensing"
    - "mask r cnn"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "animal detection in man made environments"):
  with members:
    - "looking fast and slow memory guided mobile video object detection"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "the iwildcam 2019 challenge dataset"
    - "searching for mobilenetv3"
    - "large scale interactive object segmentation with human annotators"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning for generic object detection a survey"

Connected through hyperedge HE-4 (induced by source paper "count crop and recognise fine grained recognition in the wild"):
  with members:
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "face recognition primates in the wild"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "c wsl count guided weakly supervised localization"
    - "the inaturalist species classification and detection dataset"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "ssd single shot multibox detector"

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)