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: "sedar semantic detection and ranging humans can localise without lidar can robots"
  Abstract: How does a person work out their location using a floorplan? It is probably safe to say that we do not explicitly measure depths to every visible surface and try to match them against different pose estimates in the floorplan. And yet, this is exactly how most robotic scan-matching algorithms operate. Similarly, we do not extrude the 2D geometry present in the floorplan into 3D and try to align it to the real-world. And yet, this is how most vision-based approaches localise. #R##N#Humans do the exact opposite. Instead of depth, we use high level semantic cues. Instead of extruding the floorplan up into the third dimension, we collapse the 3D world into a 2D representation. Evidence of this is that many of the floorplans we use in everyday life are not accurate, opting instead for high levels of discriminative landmarks. #R##N#In this work, we use this insight to present a global localisation approach that relies solely on the semantic labels present in the floorplan and extracted from RGB images. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.

Connected through hyperedge HE-1 (induced by source paper "robot localization in floor plans using a room layout edge extraction network"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "floorplan priors for joint camera pose and room layout estimation"
    - "identity mappings in deep residual networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "particle filter networks with application to visual localization"):
  with members:
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "filtering variational objectives"
    - "value prediction network"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-3 (induced by source paper "scene coordinate and correspondence learning for image based localization"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "semantic visual localization"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "generic 3d representation via pose estimation and matching"
    - "image based localization using lstms for structured feature correlation"
    - "dsac differentiable ransac for camera localization"

Connected through hyperedge HE-4 (induced by source paper "localisation via deep imagination learn the features not the map"):
  with members:
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "geometry aware learning of maps for camera localization"
    - "image based localization using lstms for structured feature correlation"
    - "dsac differentiable ransac for camera localization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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.RO(Robotics)