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: "semantic texture for robust dense tracking"
  Abstract: We argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values. We use a straightforward Lucas-Kanade formulation of image alignment, with a schedule of iterations over the coarse-to-fine levels of a pyramid, and simply replace the usual image pyramid by the hierarchy of convolutional feature maps from a pre-trained CNN. The resulting dense alignment performance is much more robust to lighting and other variations, as we show by camera rotation tracking experiments on time-lapse sequences captured over many hours. Looking towards the future of scene representation for real-time visual SLAM, we further demonstrate that a selection using simple criteria of a small number of the total set of features output by a CNN gives just as accurate but much more efficient tracking performance.

Connected through hyperedge HE-1 (induced by source paper "gn net the gauss newton loss for multi weather relocalization"):
  with members:
    - "regnet learning the optimization of direct image to image pose registration"
    - "open3d a modern library for 3d data processing"
    - "neighbourhood consensus networks"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "taking a deeper look at the inverse compositional algorithm"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-2 (induced by source paper "hierarchical metric learning and matching for 2d and 3d geometric correspondences"):
  with members:
    - "deep supervision with intermediate concepts"
    - "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"
    - "learning spread out local feature descriptors"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "sfm net learning of structure and motion from video"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-3 (induced by source paper "ba net dense bundle adjustment network"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "gslam initialization robust monocular visual slam via global structure from motion"
    - "dilated residual networks"
    - "optnet differentiable optimization as a layer in neural networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "regnet learning the optimization of direct image to image pose registration"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "ls net learning to solve nonlinear least squares for monocular stereo"
    - "aligning across large gaps in time"
    - "deep fundamental matrix estimation without correspondences"
    - "ba net dense bundle adjustment network"
    - "dilated residual networks"
    - "relative camera pose estimation using convolutional neural networks"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-5 (induced by source paper "futuremapping the computational structure of spatial ai systems"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
    - "da rnn semantic mapping with data associated recurrent 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)