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: "deep deformation network for object landmark localization"
  Abstract: We propose a novel cascaded framework, namely deep deformation network (DDN), for localizing landmarks in non-rigid objects. The hallmarks of DDN are its incorporation of geometric constraints within a convolutional neural network (CNN) framework, ease and efficiency of training, as well as generality of application. A novel shape basis network (SBN) forms the first stage of the cascade, whereby landmarks are initialized by combining the benefits of CNN features and a learned shape basis to reduce the complexity of the highly nonlinear pose manifold. In the second stage, a point transformer network (PTN) estimates local deformation parameterized as thin-plate spline transformation for a finer refinement. Our framework does not incorporate either handcrafted features or part connectivity, which enables an end-to-end shape prediction pipeline during both training and testing. In contrast to prior cascaded networks for landmark localization that learn a mapping from feature space to landmark locations, we demonstrate that the regularization induced through geometric priors in the DDN makes it easier to train, yet produces superior results. The efficacy and generality of the architecture is demonstrated through state-of-the-art performances on several benchmarks for multiple tasks such as facial landmark localization, human body pose estimation and bird part localization.

Connected through hyperedge HE-1 (induced by source paper "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "towards a deep learning framework for unconstrained face detection"
    - "spatial transformer networks"
    - "pose invariant 3d face alignment"
    - "effective face frontalization in unconstrained images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep kinematic pose regression"):
  with members:
    - "convolutional pose machines"
    - "training a feedback loop for hand pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "structured prediction of 3d human pose with deep neural networks"
    - "model based deep hand pose estimation"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "real time fine grained categorization with accuracy and interpretability"):
  with members:
    - "convolutional pose machines"
    - "fine grained pose prediction normalization and recognition"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deepm a deep part based model for object detection and semantic part localization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal 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)