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: "generating realtime motion plans from complex natural language commands using dynamic grounding graphs"
  Abstract: We present an algorithm for combining natural language processing (NLP) and realtime robot motion planning to automatically generate safe robot movements. We present a novel method to map the complex natural language commands into appropriate cost function and constraint parameters for optimization-based motion planning. Given NLP commands, we generate a factor graph named Dynamic Grounding Graph (DGG). The coefficients of this factor graph are learned based on conditional random fields and used to dynamically generate the constraints for motion planning. We directly map the cost function to the parameters of the motion planner to generate collision-free and smooth paths in complex scenes with moving obstacles, by modeling intricate motion planning parameters such as speed, orientation, position, as well as smoothness, repulsion, and avoidance. We highlight the performance of our approach in a simulated environment as well as via a human interacting with a 7-DOF Fetch robot with complex NLP commands.

Connected through hyperedge HE-1 (induced by source paper "safe navigation with human instructions in complex scenes"):
  with members:
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "efficient generation of motion plans from attribute based natural language instructions using dynamic constraint mapping"
    - "proximal policy optimization algorithms"
    - "mask r cnn"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "generalized grounding graphs a probabilistic framework for understanding grounded commands"

Connected through hyperedge HE-2 (induced by source paper "efficient generation of motion plans from attribute based natural language instructions using dynamic constraint mapping"):
  with members:
    - "generalized grounding graphs a probabilistic framework for understanding grounded commands"

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)