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: "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.

Connected through hyperedge HE-1 (induced by source paper "soft ranking label encoding for robust facial age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "c3ae exploring the limits of compact model for age estimation"
    - "deep representation learning with part loss for person re identification"
    - "a coupled evolutionary network for age estimation"
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "deep label distribution learning with label ambiguity"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-2 (induced by source paper "multimodal age and gender classification using ear and profile face images"):
  with members:
    - "age and gender classification from ear images"
    - "domain adaptation for ear recognition using deep convolutional neural networks"
    - "ear recognition more than a survey"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"

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)