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 "a unified tensor based active appearance face model"):
  Abstract: Appearance variations result in many difficulties in face image analysis. To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces. In contrast with the classical Tensor-based AAM (T-AAM), the proposed UT-AAM has four advantages: First, for each type of face information, namely shape and texture, we construct a tensor model capturing all relevant appearance variations. This unified tensor model contrasts with the variation-specific models of T-AAM. Second, a strategy for dealing with self-occluded faces is proposed to obtain consistent shape and texture representations of faces across large pose variations. Third, our UT-AAM is capable of constructing the model from an incomplete training dataset, using tensor completion methods. Last, we use an effective cascaded-regression-based method for UT-AAM fitting. With these improvements, the utility of UT-AAM in practice is considerably enhanced in comparison with the classical T-AAM. As an example, we demonstrate the improvements in training facial landmark detectors through the use of UT-AAM to synthesise a large number of virtual samples. Experimental results obtained using the Multi-PIE and 300-W face datasets demonstrate the merits of the proposed approach.

Includes members:
  Member "deep appearance models a deep boltzmann machine approach for face modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition")
  Member "dynamic attention controlled cascaded shape regression exploiting training data augmentation and fuzzy set sample weighting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pixel in pixel net towards efficient facial landmark detection in the wild")
  Member "fitting 3d morphable models using local features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high accuracy face geometry capture using a smartphone video")

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)