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: "the conditional lucas kanade algorithm"
  Abstract: The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense image and object alignment. The approach is efficient as it attempts to model the connection between appearance and geometric displacement through a linear relationship that assumes independence across pixel coordinates. A drawback of the approach, however, is its generative nature. Specifically, its performance is tightly coupled with how well the linear model can synthesize appearance from geometric displacement, even though the alignment task itself is associated with the inverse problem. In this paper, we present a new approach, referred to as the Conditional LK algorithm, which: (i) directly learns linear models that predict geometric displacement as a function of appearance, and (ii) employs a novel strategy for ensuring that the generative pixel independence assumption can still be taken advantage of. We demonstrate that our approach exhibits superior performance to classical generative forms of the LK algorithm. Furthermore, we demonstrate its comparable performance to state-of-the-art methods such as the Supervised Descent Method with substantially less training examples, as well as the unique ability to "swap" geometric warp functions without having to retrain from scratch. Finally, from a theoretical perspective, our approach hints at possible redundancies that exist in current state-of-the-art methods for alignment that could be leveraged in vision systems of the future.

Connected through hyperedge HE-1 (induced by source paper "photometric mesh optimization for video aligned 3d object reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "proxy templates for inverse compositional photometric bundle adjustment"
    - "unsupervised learning of depth and ego motion from video"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-2 (induced by source paper "st gan spatial transformer generative adversarial networks for image compositing"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "began boundary equilibrium generative adversarial networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "unsupervised learning of depth and ego motion from video"
    - "deformable convolutional networks"
    - "unsupervised image to image translation networks"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "deep lk for efficient adaptive object tracking"):
  with members:
    - "eco efficient convolution operators for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "bit planes dense subpixel alignment of binary descriptors"
    - "learning to track at 100 fps with deep regression networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "staple complementary learners for real time tracking"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "inverse compositional spatial transformer networks"):
  with members:
    - "identity mappings in deep residual networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "do convnets learn correspondence"
    - "caffe convolutional architecture for fast feature embedding"

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)