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: "supervised descent method for solving nonlinear least squares problems in computer vision"
  Abstract: Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved with nonlinear optimization methods. It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, second order descent methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical, and (2) the Hessian may be large and not positive definite. To address these issues, this paper proposes generic descent maps, which are average "descent directions" and rescaling factors learned in a supervised fashion. Using generic descent maps, we derive a practical algorithm - Supervised Descent Method (SDM) - for minimizing Nonlinear Least Squares (NLS) problems. During training, SDM learns a sequence of decent maps that minimize the NLS. In testing, SDM minimizes the NLS objective using the learned descent maps without computing the Jacobian or the Hessian. We prove the conditions under which the SDM is guaranteed to converge. We illustrate the effectiveness and accuracy of SDM in three computer vision problems: rigid image alignment, non-rigid image alignment, and 3D pose estimation. In particular, we show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code has been made available at www.humansensing.cs.cmu.edu/intraface.

Connected through hyperedge HE-1 (induced by source paper "discriminative optimization theory and applications to computer vision"):
  with members:
    - "a general and adaptive robust loss function"
    - "learning to learn by gradient descent by gradient descent"
    - "deep image homography estimation"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "point set registration coherent point drift"

Connected through hyperedge HE-2 (induced by source paper "simultaneous regression and feature learning for facial landmarking"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "facial landmark detection a literature survey"
    - "a functional regression approach to facial landmark tracking"

Connected through hyperedge HE-3 (induced by source paper "a functional regression approach to facial landmark tracking"):
  with members:
    - "a novel performance evaluation methodology for single target trackers"
    - "cascaded continuous regression for real time incremental face tracking"

Connected through hyperedge HE-4 (induced by source paper "efficient subpixel refinement with symbolic linear predictors"):
  with members:
    - "inverse compositional spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "brain4cars car that knows before you do via sensory fusion deep learning architecture"):
  with members:
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "planit a crowdsourcing approach for learning to plan paths from large scale preference feedback"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "deep speech scaling up end to end speech recognition"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "the conditional lucas kanade algorithm"):
  with members:
    - "bit planes dense subpixel alignment of binary descriptors"

Connected through hyperedge HE-7 (induced by source paper "cascaded continuous regression for real time incremental face tracking"):
  with members:
    - "a novel performance evaluation methodology for single target trackers"

Connected through hyperedge HE-8 (induced by source paper "mirror mirror on the wall tell me is the error small"):
  with members:
    - "beyond physical connections tree models in human pose estimation"
    - "intriguing properties of neural networks"

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)