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: "reactive planar manipulation with convex hybrid mpc"
  Abstract: This paper presents a reactive controller for planar manipulation tasks that leverages machine learning to achieve real-time performance. The approach is based on a Model Predictive Control (MPC) formulation, where the goal is to find an optimal sequence of robot motions to achieve a desired object motion. Due to the multiple contact modes associated with frictional interactions, the resulting optimization program suffers from combinatorial complexity when tasked with determining the optimal sequence of modes. #R##N#To overcome this difficulty, we formulate the search for the optimal mode sequences offline, separately from the search for optimal control inputs online. Using tools from machine learning, this leads to a convex hybrid MPC program that can be solved in real-time. We validate our algorithm with the problem of pushing a planar object of arbitrary shape with an arbitrary number of contact points.

Connected through hyperedge HE-1 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback"):
  with members:
    - "tactile mapping and localization from high resolution tactile imprints"
    - "manipulation by feel touch based control with deep predictive models"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "in hand object stabilization by independent finger control"
    - "in hand manipulation via motion cones"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "analysis and observations from the first amazon picking challenge"

Connected through hyperedge HE-2 (induced by source paper "describing physics for physical reasoning force based sequential manipulation planning"):
  with members:
    - "probabilistic framework for constrained manipulations and task and motion planning under uncertainty"
    - "a quasi static model and simulation approach for pushing grasping and jamming"
    - "a data efficient approach to precise and controlled pushing"
    - "towards combining htn planning and geometric task planning"

Connected through hyperedge HE-3 (induced by source paper "accurate vision based manipulation through contact reasoning"):
  with members:
    - "let s push things forward a survey on robot pushing"
    - "combining physical simulators and object based networks for control"
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "in hand manipulation via motion cones"
    - "self supervised visual planning with temporal skip connections"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Connected through hyperedge HE-4 (induced by source paper "local trajectory stabilization for dexterous manipulation via piecewise affine approximations"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "controller synthesis for discrete time hybrid polynomial systems via occupation measures"
    - "sampling based polytopic trees for approximate optimal control of piecewise affine systems"
    - "learning dexterous in hand manipulation"
    - "in hand manipulation via motion cones"
    - "deep visual foresight for planning robot motion"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-5 (induced by source paper "contact aware controller design for complementarity systems"):
  with members:
    - "a quasi static model and simulation approach for pushing grasping and jamming"
    - "modeling and analysis of non unique behaviors in multiple frictional impacts"
    - "manipulation by feel touch based control with deep predictive models"
    - "gelslim a high resolution compact robust and calibrated tactile sensing finger"
    - "leveraging contact forces for learning to grasp"
    - "lvis learning from value function intervals for contact aware robot controllers"

Connected through hyperedge HE-6 (induced by source paper "hybrid differential dynamic programming for planar manipulation primitives"):
  with members:
    - "tactile dexterity manipulation primitives with tactile feedback"
    - "in hand manipulation via motion cones"
    - "hybrid control trajectory optimization under uncertainty"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"
    - "a framework for fine robotic assembly"

Connected through hyperedge HE-7 (induced by source paper "locomotion planning through a hybrid bayesian trajectory optimization"):
  with members:
    - "learning a structured neural network policy for a hopping task"
    - "sequential linear quadratic optimal control for nonlinear switched systems"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"

Connected through hyperedge HE-8 (induced by source paper "a model predictive approach for online mobile manipulation of nonholonomic objects using learned dynamics"):
  with members:
    - "uncertainty averse pushing with model predictive path integral control"
    - "hybrid control trajectory optimization under uncertainty"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"

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)