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: "footstep planning for autonomous walking over rough terrain"
  Abstract: To increase the speed of operation and reduce operator burden, humanoid robots must be able to function autonomously, even in complex, cluttered environments. For this to be possible, they must be able to quickly and efficiently compute desired footsteps to reach a goal. In this work, we present a new A* footstep planner that utilizes a planar region representation of the environment enable footstep planning over rough terrain. To increase the number of available footholds, we present an approach to allow the use of partial footholds during the planning process. The footstep plan solutions are then post-processed to capture better solutions that lie between the lattice discretization of the footstep graph. We then demonstrate this planner over a variety of virtual and real world environments, including some that require partial footholds and rough terrain using the Atlas and Valkyrie humanoid robots.

Connected through hyperedge HE-1 (induced by source paper "a robust model based biped locomotion framework based on three mass model from planning to control"):
  with members:
    - "a fast and stable omnidirectional walking engine for the nao humanoid robot"
    - "a whole body model predictive control scheme including external contact forces and com height variations"
    - "3lp a linear 3d walking model including torso and swing dynamics"

Connected through hyperedge HE-2 (induced by source paper "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "learning agile and dynamic motor skills for legged robots"
    - "fast and continuous foothold adaptation for dynamic locomotion through cnns"
    - "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-3 (induced by source paper "sl1m sparse l1 norm minimization for contact planning on uneven terrain"):
  with members:
    - "fast uav trajectory optimization using bilevel optimization with analytical gradients"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"

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)