human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "reactive task and motion planning for robust whole body dynamic locomotion in constrained environments"):
  Abstract: Contact-based decision and planning methods are becoming increasingly important to endow higher levels of autonomy for legged robots. Formal synthesis methods derived from symbolic systems have great potential for reasoning about high-level locomotion decisions and achieving complex maneuvering behaviors with correctness guarantees. This study takes a first step toward formally devising an architecture composed of task planning and control of whole-body dynamic locomotion behaviors in constrained and dynamically changing environments. At the high level, we formulate a two-player temporal logic game between the multi-limb locomotion planner and its dynamic environment to synthesize a winning strategy that delivers symbolic locomotion actions. These locomotion actions satisfy the desired high-level task specifications expressed in a fragment of temporal logic. Those actions are sent to a robust finite transition system that synthesizes a locomotion controller that fulfills state reachability constraints. This controller is further executed via a low-level motion planner that generates feasible locomotion trajectories. We construct a set of dynamic locomotion models for legged robots to serve as a template library for handling diverse environmental events. We devise a replanning strategy that takes into consideration sudden environmental changes or large state disturbances to increase the robustness of the resulting locomotion behaviors. We formally prove the correctness of the layered locomotion framework guaranteeing a robust implementation by the motion planning layer. Simulations of reactive locomotion behaviors in diverse environments indicate that our framework has the potential to serve as a theoretical foundation for intelligent locomotion behaviors.

Includes members:
  Member "deepmimic example guided deep reinforcement learning of physics based character skills":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "artificial intelligence for prosthetics challenge solutions")
  Member "robust abstractions for control synthesis completeness via robustness for linear time properties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flexible computational pipelines for robust abstraction based control synthesis")
  Member "combined top down and bottom up approaches to performance guaranteed integrated task and motion planning of cooperative multi agent systems":
    also connected through other hyperedges:
      (none)
  Member "multi contact walking pattern generation based on model preview control of 3d com accelerations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "biped stabilization by linear feedback of the variable height inverted pendulum model")
  Member "robust temporal logic model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prescribed performance control guided policy improvement for satisfying signal temporal logic tasks")
  Member "zmp support areas for multi contact mobility under frictional constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "biped stabilization by linear feedback of the variable height inverted pendulum model")
  Member "robust online monitoring of signal temporal logic":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "paracosm a language and tool for testing autonomous driving systems")
  Member "probably approximately correct mdp learning and control with temporal logic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")

Question: Please tell me which class the source paper 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)