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 "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"):
  Abstract: Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a classification network is followed by a second and modular classification network, trained in an end to end fashion to obtain accurate landmark points. Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. Extensive experimentation shows that conditioning on pose reduces the localization error by making it agnostic to face pose. The proposed model can be extended to yield variable number of landmark points and hence broadening its applicability to other datasets. Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto $15\%$ reduction in error compared to state-of-the-art methods for extreme and medium pose face images from challenging datasets including AFLW, AFW, COFW and IBUG.

Includes members:
  Member "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sm nas structural to modular neural architecture search for object detection")
  Member "face alignment robust to pose expressions and occlusions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tree gated deep regressor ensemble for face alignment in the wild")
  Member "large pose 3d face reconstruction from a single image via direct volumetric cnn regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "pose invariant face alignment with a single cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces")
  Member "deep heterogeneous feature fusion for template based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks")
  Member "the do s and don ts for cnn based face verification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "dense face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "face detection with feature pyramids and landmarks")
  Member "simultaneous facial landmark detection pose and deformation estimation under facial occlusion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces")

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)