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: "fast and continuous foothold adaptation for dynamic locomotion through cnns"
  Abstract: Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the swing phase trajectory of each leg. To efficiently adapt the landing position, we implement a self-supervised foothold classifier based on a convolutional neural network. Our method results in an up to 200 times faster computation with respect to the full-blown heuristics. Our goal is to react to visual stimuli from the environment, bridging the gap between blind reactive locomotion and purely vision-based planning strategies. We assess the performance of our method on the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe foothold adaptation is clearly demonstrated by the overall robot behavior.

Connected through hyperedge HE-1 (induced by source paper "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"):
  with members:
    - "footstep planning for autonomous walking over rough terrain"
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "learning agile and dynamic motor skills for legged robots"
    - "learning to walk via deep reinforcement learning"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-2 (induced by source paper "mpc based controller with terrain insight for dynamic legged locomotion"):
  with members:
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "automatic differentiation of rigid body dynamics for optimal control and estimation"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-3 (induced by source paper "rolling in the deep hybrid locomotion for wheeled legged robots using online trajectory optimization"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "keep rollin whole body motion control and planning for wheeled quadrupedal robots"
    - "efficient kinematic planning for mobile manipulators with non holonomic constraints using optimal control"

Connected through hyperedge HE-4 (induced by source paper "robust legged robot state estimation using factor graph optimization"):
  with members:
    - "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "on manifold preintegration for real time visual inertial odometry"

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)