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 "fine grained age estimation in the wild with attention lstm networks"):
  Abstract: Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision which has a wide range of practical application value. Concerning the problem that accuracy of age estimation of face images in the wild are relatively low for existing methods, where they take into account only the whole features of face image while neglecting the fine-grained features of age-sensitive area, we propose a method based on Attention LSTM network for Fine-Grained age estimation in the wild based on the idea of Fine-Grained categories and visual attention mechanism. This method combines ResNets or RoR models with LSTM unit to construct AL-ResNets or AL-RoR networks to extract age-sensitive local regions, which effectively improves age estimation accuracy. Firstly, ResNets or RoR model pre-trained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune ResNets or RoR on the target age datasets to extract the global features of face images. To extract the local characteristics of age-sensitive areas, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, the age group classification experiment is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and LAP datasets. By combining the global and local features, we got our final prediction results. Our experiments illustrate the effectiveness of AL-ResNets or AL-RoR for age estimation in the wild, where it achieves new state-of-the-art performance than all other CNN methods on the Adience, MORPH Album 2, FG-NET and LAP datasets.

Includes members:
  Member "residual networks of residual networks multilevel residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks")
  Member "deep label distribution learning with label ambiguity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "age group and gender estimation in the wild with deep ror architecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "competing ratio loss for discriminative multi class image classification")
  Member "video summarization with attention based encoder decoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning for task driven video summarization")
  Member "dager deep age gender and emotion recognition using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models")
  Member "pyramidal ror for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "autoslim towards one shot architecture search for channel numbers")
  Member "apparent age estimation using ensemble of deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "soft ranking label encoding for robust facial age estimation")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on feature normalization and data augmentation")

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)