human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an experimental evaluation of covariates effects on unconstrained face verification"):
  Abstract: Covariates are factors that have a debilitating influence on face verification performance. In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance. To study the first problem, we implement five state-of-the-art deep convolutional networks and evaluate them on three challenging covariates datasets. In total, seven covariates are considered: pose (yaw and roll), age, facial hair, gender, indoor/outdoor, occlusion (nose and mouth visibility, and forehead visibility), and skin tone. We first report the performance of each individual network on the overall protocol and use the score-level fusion method to analyze each covariate. Some of the results confirm and extend the findings of previous studies, and others are new findings that were rarely mentioned previously or did not show consistent trends. For the second problem, we demonstrate that with the assistance of gender information, the quality of a precurated noisy large-scale face dataset for face recognition can be further improved. After retraining the face recognition model using the curated data, performance improvement is observed at low false acceptance rates.

Includes members:
  Member "the do s and don ts for cnn based face verification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "l2 constrained softmax loss for discriminative face verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalizing face representation with unlabeled data")
  Member "a proximity aware hierarchical clustering of faces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised domain specific deblurring via disentangled representations")
  Member "level playing field for million scale face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "a comprehensive analysis of deep learning based representation for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "face recognition a novel multi level taxonomy based survey")
  Member "triplet probabilistic embedding for face verification and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "an all in one convolutional neural network for face analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")

Question: Please tell me which class the source paper 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)