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 "learning deep representation for face alignment with auxiliary attributes"):
  Abstract: In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attributes, such as gender, expression, and appearance attributes. This is non-trivial since different attribute inference tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, which not only learns the inter-task correlation but also employs dynamic task coefficients to facilitate the optimization convergence when learning multiple complex tasks. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing face alignment methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art methods based on cascaded deep model.

Includes members:
  Member "surpassing human level face verification performance on lfw with gaussianface":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "person recognition in personal photo collections")
  Member "recover canonical view faces in the wild with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "face hallucination with finishing touches")
  Member "deep learning face representation by joint identification verification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive deep metric embeddings for person re identification under occlusions")
  Member "deep learning multi view representation for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a study on various state of the art of the art face recognition system using deep learning techniques")
  Member "deep regression for face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a fast online cascaded regression algorithm for face alignment")
  Member "heterogeneous multi task learning for human pose estimation with deep convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep multi task augmented feature learning via hierarchical graph neural network")
  Member "a convex formulation for learning task relationships in multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a simple general approach to balance task difficulty in multi task learning")

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)