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: "on low resolution face recognition in the wild comparisons and new techniques"
  Abstract: Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition task remains challenging, especially when the low-resolution faces are captured under non-ideal conditions, which is widely prevalent in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, non-uniform lighting, and non-frontal face pose. In this paper, we analyze the face recognition techniques using data captured under low-quality conditions in the wild. We provide a comprehensive analysis of the experimental results for two of the most important applications in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance. The following three contributions are made:  (i)  we conduct experiments to evaluate super-resolution methods for low-resolution face recognition;  (ii)  we study face re-identification on various public face datasets, including real surveillance and low-resolution subsets of large-scale datasets, presenting a baseline result for several deep learning-based approaches, and improve them by introducing a generative adversarial network pre-training approach and fully convolutional architecture; and  (iii)  we explore the low-resolution face identification by employing a state-of-the-art supervised discriminative learning approach. The evaluations are conducted on challenging portions of the SCface and UCCSface datasets.

Connected through hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-2 (induced by source paper "low resolution face recognition in the wild via selective knowledge distillation"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "low resolution face recognition in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"
    - "data distillation towards omni supervised learning"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-3 (induced by source paper "attribute guided coupled gan for cross resolution face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "style and content disentanglement in generative adversarial networks"
    - "synthesis based robust low resolution face recognition"

Connected through hyperedge HE-4 (induced by source paper "extreme low resolution activity recognition with spatial temporal attention transfer"):
  with members:
    - "efficient video classification using fewer frames"
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (induced by source paper "dual directed capsule network for very low resolution image recognition"):
  with members:
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "brain tumor type classification via capsule networks"
    - "recognizing disguised faces in the wild"
    - "dynamic routing between capsules"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "studying very low resolution recognition using deep networks"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-6 (induced by source paper "graph representation for face analysis in image collections"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "a style based generator architecture for generative adversarial networks"
    - "face clustering representation and pairwise constraints"
    - "real time convolutional neural networks for emotion and gender classification"
    - "fine grained head pose estimation without keypoints"
    - "face detection using deep learning an improved faster rcnn approach"
    - "clustering millions of faces by identity"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "face recognition in low quality images a survey"):
  with members:
    - "coupled learning for facial deblur"
    - "super identity convolutional neural network for face hallucination"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "low resolution face recognition"
    - "deep semantic face deblurring"
    - "surveillance face recognition challenge"
    - "face super resolution through wasserstein gans"
    - "s 3 fd single shot scale invariant face detector"

Connected through hyperedge HE-8 (induced by source paper "low resolution face recognition using a two branch deep convolutional neural network architecture"):
  with members:
    - "exploring factors for improving low resolution face recognition"
    - "robust face recognition via multimodal deep face representation"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "visualizing and understanding convolutional networks"

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)