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 "soft ranking label encoding for robust facial age estimation"):
  Abstract: Automatic facial age estimation can be used in a wide range of real-world applications. However, this process is challenging due to the randomness and slowness of the aging process. Accordingly, in this paper, we propose a comprehensive framework aimed at overcoming the challenges associated with facial age estimation. First, we propose a novel age encoding method, referred to as 'Soft-ranking', which encodes two important properties of facial age, i.e., the ordinal property and the correlation between adjacent ages. Therefore, Soft-ranking provides a richer supervision signal for training deep models. Moreover, we also carefully analyze existing evaluation protocols for age estimation, finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods. Finally, since existing face databases for age estimation are generally small, deep models tend to suffer from an overfitting problem. To address this issue, we propose a novel regularization strategy to encourage deep models to learn more robust features from facial parts for age estimation purposes. Extensive experiments indicate that the proposed techniques improve the age estimation performance; moreover, we achieve state-of-the-art performance on the three most popular age databases, $i.e.$, Morph II, CLAP2015, and CLAP2016.

Includes members:
  Member "bridgenet a continuity aware probabilistic network for age estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self paced deep regression forests for facial age estimation")
  Member "c3ae exploring the limits of compact model for age estimation":
    also connected through other hyperedges:
      (none)
  Member "deep representation learning with part loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning shape representations for clothing variations in person re identification")
  Member "fine grained age estimation in the wild with attention lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal age and gender classification using ear and profile face images")
  Member "a coupled evolutionary network for age estimation":
    also connected through other hyperedges:
      (none)
  Member "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep weakly supervised domain adaptation for pain localization in videos")
  Member "deep label distribution learning with label ambiguity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "heterogeneous face attribute estimation a deep multi task learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep multi task multi label cnn for effective facial attribute classification")

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)