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 "saf bage salient approach for facial soft biometric classification age gender and facial expression"):
  Abstract: How can we improve the facial soft-biometric classification with help of the human visual system? This paper explores the use of saliency which is equivalent to the human visual system to classify Age, Gender and Facial Expression soft-biometric for facial images. Using the Deep Multi-level Network (ML-Net) [1] and off-the-shelf face detector [2], we propose our approach - SAF-BAGE, which first detects the face in the test image, increases the Bounding Box (B-Box) margin by 30%, finds the saliency map using ML-Net, with 30% reweighted ratio of saliency map, it multiplies with the input cropped face and extracts the Convolutional Neural Networks (CNN) predictions on the multiplied reweighted salient face. Our CNN uses the model AlexNet [3], which is pre-trained on ImageNet. The proposed approach surpasses the performance of other approaches, increasing the state-of-the-art by approximately 0.8% on the widely-used Adience [28] dataset for Age and Gender classification and by nearly 3% on the recent AffectNet [36] dataset for Facial Expression classification. We hope our simple, reproducible and effective approach will help ease future research in facial soft-biometric classification using saliency.

Includes members:
  Member "dager deep age gender and emotion recognition using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models")
  Member "convolutional neural networks for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyperparameters optimization for deep learning based emotion prediction for human robot interaction")
  Member "cnn architectures for large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal dense video captioning")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "visual saliency based on multiscale deep features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "ask me anything dynamic memory networks for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "effective face frontalization in unconstrained images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")

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)