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: "patch based probabilistic image quality assessment for face selection and improved video based face recognition"
  Abstract: In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the ‘best’ of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "representation learning by rotating your faces"):
  with members:
    - "on learning 3d face morphable model from in the wild images"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "additive margin softmax for face verification"
    - "nonlinear 3d face morphable model"
    - "began boundary equilibrium generative adversarial networks"
    - "pose invariant face alignment with a single cnn"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "towards large pose face frontalization in the wild"

Connected through hyperedge HE-2 (induced by source paper "cross domain face synthesis using a controllable gan"):
  with members:
    - "representation learning by rotating your faces"
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "conditional image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "robust conditional generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "toward multimodal image to image translation"

Connected through hyperedge HE-3 (induced by source paper "a paired sparse representation model for robust face recognition from a single sample"):
  with members:
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "cosface large margin cosine loss for deep face recognition"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "sphereface deep hypersphere embedding for face recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "domain specific face synthesis for video face recognition from a single sample per person"):
  with members:
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "do we really need to collect millions of faces for effective face recognition"
    - "learning from simulated and unsupervised images through adversarial training"
    - "face recognition using deep multi pose representations"
    - "image to image translation with conditional adversarial networks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"

Connected through hyperedge HE-5 (induced by source paper "attentive deep regression networks for real time visual face tracking in video surveillance"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "visual object tracking performance measures revisited"
    - "learning to track at 100 fps with deep regression networks"
    - "deep learning in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "decoupled deep neural network for semi supervised semantic segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "maximum likelihood approximate nearest neighbor method in real time image recognition"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "viplfacenet an open source deep face recognition sdk"

Connected through hyperedge HE-7 (induced by source paper "residual codean autoencoder for facial attribute analysis"):
  with members:
    - "adversarial diversity and hard positive generation"
    - "face attribute prediction using off the shelf cnn features"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "affact alignment free facial attribute classification technique"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "deep face quality assessment"):
  with members:
    - "automatic face image quality prediction"
    - "facenet a unified embedding for face recognition 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)