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: "a mpc walking framework with external contact forces"
  Abstract: In this work, we present an extension to a linear Model Predictive Control (MPC) scheme that plans external contact forces for the robot when given multiple contact locations and their corresponding friction cone. To accomplish this we set up a two step optimization problem. In the first optimization, we compute the Center of Mass (CoM) trajectory, foot step locations, and introduce slack variables to account for violating the imposed constraints on the Center of Pressure (CoP). We then use the slack variables to trigger the second optimization, in which we calculate the optimal external force that compensates for the CoP tracking error. This optimization considers multiple contacts with the environment by formulating the problem as a Mixed Integer Quadratic Program (MIQP) that can be solved at the order of 100 Hz. Once contact is created, the MIQP collapses to a single Quadratic Program (QP) that can be solved in real time $<$ 1kHz. Simulations show that the presented control scheme can withstand disturbances 2-5x larger with the additional force provide by a hand contact when considering delays and 3-6x larger when contact is already made.

Connected through hyperedge HE-1 (induced by source paper "learning generalizable locomotion skills with hierarchical reinforcement learning"):
  with members:
    - "data efficient reinforcement learning for legged robots"
    - "curious ilqr resolving uncertainty in model based rl"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "learning agile and dynamic motor skills for legged robots"
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "sim to real learning agile locomotion for quadruped robots"

Connected through hyperedge HE-2 (induced by source paper "whole body mpc for a dynamically stable mobile manipulator"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "frequency aware model predictive control"
    - "hierarchical optimization for whole body control of wheeled inverted pendulum humanoids"
    - "a whole body model predictive control scheme including external contact forces and com height variations"
    - "real time motion planning of legged robots a model predictive control approach"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "automatic differentiation of rigid body dynamics for optimal control and estimation"

Connected through hyperedge HE-3 (induced by source paper "robust humanoid contact planning with learned zero and one step capturability prediction"):
  with members:
    - "unified multi contact fall mitigation planning for humanoids via contact transition tree optimization"
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "capturability based pattern generation for walking with variable height"
    - "walking stabilization using step timing and location adjustment on the humanoid robot atlas"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "structured contact force optimization for kino dynamic motion generation"
    - "step timing adjustment a step toward generating robust gaits"

Connected through hyperedge HE-4 (induced by source paper "a whole body model predictive control scheme including external contact forces and com height variations"):
  with members:
    - "walking stabilization using step timing and location adjustment on the humanoid robot atlas"
    - "pattern generation for walking on slippery terrains"
    - "a robust walking controller based on online step location and duration optimization for bipedal locomotion"
    - "stepping stabilization using a combination of dcm tracking and step adjustment"

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)