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: "deep spatiotemporal models for robust proprioceptive terrain classification"
  Abstract: Terrain classification is a critical component of any autonomous mobile robot system operating in unknown real-world environments. Over the years, several proprioceptive terrain classification techniques have been introduced to increase robustness or act as a fallback for traditional vision based approaches. However, they lack widespread adaptation due to various factors that include inadequate accuracy, robustness and slow run-times. In this paper, we use vehicle-terrain interaction sounds as a proprioceptive modality and propose a deep long-short term memory based recurrent model that captures both the spatial and temporal dynamics of such a problem, thereby overcoming these past limitations. Our model consists of a new convolution neural network architecture that learns deep spatial features, complemented with long-short term memory units that learn complex temporal dynamics. Experiments on two extensive datasets collected with different microphones on various indoor and outdoor terrains demonstrate st...

Connected through hyperedge HE-1 (induced by source paper "self supervised visual terrain classification from unsupervised acoustic feature learning"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "fast scnn fast semantic segmentation network"
    - "modular sensor fusion for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning long range perception using self supervision from short range sensors and odometry"

Connected through hyperedge HE-2 (induced by source paper "surface type classification for autonomous robot indoor navigation"):
  with members:
    - "deep learning for time series classification a review"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "xgboost a scalable tree boosting system"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "network in network"

Connected through hyperedge HE-3 (induced by source paper "assessment of deep convolutional neural networks for road surface classification"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "comparing apples and oranges off road pedestrian detection on the nrec agricultural person detection dataset"
    - "deep residual learning for image recognition"
    - "the unreasonable effectiveness of noisy data for fine grained recognition"
    - "rethinking the inception architecture for computer vision"

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)