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: "bridgenet a continuity aware probabilistic network for age estimation"
  Abstract: Age estimation is an important yet very challenging problem in computer vision. Existing methods for age estimation usually apply a divide-and-conquer strategy to deal with heterogeneous data caused by the non-stationary aging process. However, the facial aging process is also a continuous process, and the continuity relationship between different components has not been effectively exploited. In this paper, we propose BridgeNet for age estimation, which aims to mine the continuous relation between age labels effectively. The proposed BridgeNet consists of local regressors and gating networks. Local regressors partition the data space into multiple overlapping subspaces to tackle heterogeneous data and gating networks learn continuity aware weights for the results of local regressors by employing the proposed bridge-tree structure, which introduces bridge connections into tree models to enforce the similarity between neighbor nodes. Moreover, these two components of BridgeNet can be jointly learned in an end-to-end way. We show experimental results on the MORPH II, FG-NET and Chalearn LAP 2015 datasets and find that BridgeNet outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "soft ranking label encoding for robust facial age estimation"):
  with members:
    - "c3ae exploring the limits of compact model for age estimation"
    - "deep representation learning with part loss for person re identification"
    - "fine grained age estimation in the wild with attention lstm networks"
    - "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 "self paced deep regression forests for facial age estimation"):
  with members:
    - "soft ranking label encoding for robust facial age estimation"
    - "active self paced learning for cost effective and progressive face identification"
    - "self paced multi task clustering"
    - "label distribution learning forests"
    - "deep regression forests for age estimation"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "ordinal distribution regression for gait based age estimation"):
  with members:
    - "label distribution learning forests"
    - "deep regression forests for age estimation"
    - "squared earth mover s distance based loss for training deep neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)