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: "satellite image based localization via learned embeddings"
  Abstract: We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance variations between the images through a neural multi-view model that learns location-discriminative embeddings in which ground-level images are matched with their corresponding satellite view of the scene. We use this learned function as an observation model in a filtering framework to maintain a distribution over the vehicle's pose. We evaluate our method on different benchmark datasets and demonstrate its ability localize ground-level images in environments novel relative to training, despite the challenges of significant viewpoint and appearance variations.

Connected through hyperedge HE-1 (induced by source paper "rsl net localising in satellite images from a radar on the ground"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "conditional image to image translation"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-2 (induced by source paper "image based geo localization using satellite imagery"):
  with members:
    - "cross view image matching for geo localization in urban environments"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "deep metric learning with angular loss"
    - "revisiting im2gps in the deep learning era"
    - "localizing and orienting street views using overhead imagery"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-3 (induced by source paper "one shot imitation from observing humans via domain adaptive meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "deep episodic memory encoding recalling and predicting episodic experiences for robot action execution"
    - "virtual to real reinforcement learning for autonomous driving"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning to generalize meta learning for domain generalization"
    - "learning robot activities from first person human videos using convolutional future regression"
    - "third person imitation learning"

Connected through hyperedge HE-4 (induced by source paper "uav pose estimation using cross view geolocalization with satellite imagery"):
  with members:
    - "cross view image matching for geo localization in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "relative camera pose estimation using convolutional neural networks"
    - "revisiting im2gps in the deep learning era"
    - "localizing and orienting street views using overhead imagery"
    - "planet photo geolocation with convolutional neural networks"
    - "an overview of gradient descent optimization algorithms"
    - "wide area image geolocalization with aerial reference imagery"

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)