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: "learning deep generative spatial models for mobile robots"
  Abstract: We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unlike traditional approaches that combine engineered models for low-level features, geometry, and semantics, our approach leverages recent advances in Sum-Product Networks (SPNs) and deep learning to learn a single, universal model of the robot's spatial environment. Our model is fully probabilistic and generative, and represents a joint distribution over spatial information ranging from low-level geometry to semantic interpretations. Once learned, it is capable of solving a wide range of tasks: from semantic classification of places, uncertainty estimation, and novelty detection, to generation of place appearances based on semantic information and prediction of missing data in partial observations. Experiments on laser-range data from a mobile robot show that the proposed universal model obtains performance superior to state-of-the-art models fine-tuned to one specific task, such as Generative Adversarial Networks (GANs) or SVMs.

Connected through hyperedge HE-1 (induced by source paper "planning beyond the sensing horizon using a learned context"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "deepnav learning to navigate large cities"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning heuristic search via imitation"
    - "iqa visual question answering in interactive environments"
    - "visual semantic planning using deep successor representations"
    - "embodied question answering"

Connected through hyperedge HE-2 (induced by source paper "optimisation of overparametrized sum product networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "probabilistic deep learning using random sum product networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-3 (induced by source paper "recognizing and tracking high level human meaningful navigation features of occupancy grid maps"):
  with members:
    - "translating neuralese"
    - "navigational instruction generation as inverse reinforcement learning with neural machine translation"
    - "yolo9000 better faster stronger"
    - "deep residual learning for image recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "from pixels to buildings end to end probabilistic deep networks for large scale semantic mapping"):
  with members:
    - "structured prediction using cgans with fusion discriminator"
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "on the latent variable interpretation in sum product networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep markov random field for image modeling"
    - "structured prediction energy networks"
    - "sum product networks a new deep architecture"

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)