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: "counterfactual reasoning about intent for interactive navigation in dynamic environments"
  Abstract: Many modern robotics applications require robots to function autonomously in dynamic environments including other decision making agents, such as people or other robots. This calls for fast and scalable interactive motion planning. This requires models that take into consideration the other agent's intended actions in one's own planning. We present a real-time motion planning framework that brings together a few key components including intention inference by reasoning counterfactually about potential motion of the other agents as they work towards different goals. By using a light-weight motion model, we achieve efficient iterative planning for fluid motion when avoiding pedestrians, in parallel with goal inference for longer range movement prediction. This inference framework is coupled with a novel distributed visual tracking method that provides reliable and robust models for the current belief-state of the monitored environment. This combined approach represents a computationally efficient alternative to previously studied policy learning methods that often require significant offline training or calibration and do not yet scale to densely populated environments. We validate this framework with experiments involving multi-robot and human-robot navigation. We further validate the tracker component separately on much larger scale unconstrained pedestrian data sets.

Connected through hyperedge HE-1 (induced by source paper "active localization of gas leaks using fluid simulation"):
  with members:
    - "learning to poke by poking experiential learning of intuitive physics"
    - "a compositional object based approach to learning physical dynamics"
    - "learning visual predictive models of physics for playing billiards"

Connected through hyperedge HE-2 (induced by source paper "value propagation networks"):
  with members:
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "cooperative motion planning for non holonomic agents with value iteration networks"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "learning generalized reactive policies using deep neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "value prediction network"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "generalized value iteration networks life beyond lattices"

Connected through hyperedge HE-3 (induced by source paper "towards a unified planner for socially aware navigation"):
  with members:
    - "deepmotion learning to navigate like humans"

Connected through hyperedge HE-4 (induced by source paper "viewing robot navigation in human environment as a cooperative activity"):
  with members:
    - "hatp an htn planner for robotics"

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)