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: "material recognition cnns and hierarchical planning for biped robot locomotion on slippery terrain"
  Abstract: In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction.

Connected through hyperedge HE-1 (induced by source paper "material segmentation of multi view satellite imagery"):
  with members:
    - "context encoding for semantic segmentation"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "stacked hourglass networks for human pose estimation"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "deep ten texture encoding network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "deep visual perception for dynamic walking on discrete terrain"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "end to end learning for self driving cars"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "sgd for robot motion the effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks"):
  with members:
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

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)