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: "face recognition primates in the wild"
  Abstract: We present a new method of primate face recognition, and evaluate this method on several endangered primates, including golden monkeys, lemurs, and chimpanzees. The three datasets contain a total of 11,637 images of 280 individual primates from 14 species. Primate face recognition performance is evaluated using two existing state-of-the-art open-source systems, (i) FaceNet and (ii) SphereFace, (iii) a lemur face recognition system from literature, and (iv) our new convolutional neural network (CNN) architecture called PrimNet. Three recognition scenarios are considered: verification (1:1 comparison), and both open-set and closed-set identification (1:N search). We demonstrate that PrimNet outperforms all of the other systems in all three scenarios for all primate species tested. Finally, we implement an Android application of this recognition system to assist primate researchers and conservationists in the wild for individual recognition of primates.

Connected through hyperedge HE-1 (induced by source paper "amur tiger re identification in the wild"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "improving person re identification by attribute and identity learning"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "towards automatic identification of elephants in the wild"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "past present and future approaches using computer vision for animal re identification from camera trap data"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-2 (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"
    - "the via annotation software for images audio and video"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "efficient method for categorize animals in the wild"):
  with members:
    - "the iwildcam 2018 challenge dataset"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mixup beyond empirical risk minimization"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "improved regularization of convolutional neural networks with cutout"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "multi views embedding for cattle re identification"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "interspecies knowledge transfer for facial keypoint detection"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "instance normalization the missing ingredient for fast stylization"
    - "learning deep embeddings with histogram loss"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "primate face identification in the wild"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "deep learning face representation by joint identification verification"

Connected through hyperedge HE-6 (induced by source paper "similarity learning networks for animal individual re identification beyond the capabilities of a human observer"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning object detection methods for ecological camera trap data"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "densely connected convolutional networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (induced by source paper "distinguishing individual red pandas from their faces"):
  with members:
    - "yolo9000 better faster stronger"
    - "automated visual fin identification of individual great white sharks"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "past present and future approaches using computer vision for animal re identification from camera trap data"):
  with members:
    - "deep learning object detection methods for ecological camera trap data"
    - "dynamic routing between capsules"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "ssd single shot multibox detector"
    - "fine grained recognition in the noisy wild sensitivity analysis of convolutional neural networks approaches"
    - "automated visual fin identification of individual great white sharks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image 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)