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: "hierarchical reinforcement learning for sequencing behaviors"
  Abstract: Recent literature in the robot learning community has focused on learning robot skills that abstract out lower-level details of robot control, such as Dynamic Movement Primitives (DMPs), the options framework in hierarchical RL, and subtask policies. To fully leverage the efficacy of these macro actions, it is necessary to then sequence these primitives to achieve a given task. Our objective is to jointly learn a set of robot skills and a sequence of these learnt skills to accomplish a given task. We consider the task of navigating a robot across various environments using visual input, maximizing the distance traveled through the environment while avoiding static obstacles. Traditional planning methods to solve this problem rely on hand-crafted state representations and heuristics for planning, and often fail to generalize. In contrast, deep neural networks have proved to be powerful function approximators, successfully modeling complex control policies. In addition, the ability of such networks to learn good representations of high-dimensional sensory inputs makes them a valuable tool when dealing with visual inputs. In this project, we explore the capability of deep neural networks to learn and sequence robot skills for navigation, directly using visual input.

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)