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: "towards search based motion planning for micro aerial vehicles"
  Abstract: Search-based motion planning has been used for mobile robots in many applications. However, it has not been fully developed and applied for planning full state trajectories of Micro Aerial Vehicles (MAVs) due to their complicated dynamics and the requirement of real-time computation. In this paper, we explore a search-based motion planning framework that plans dynamically feasible, collision-free, and resolution optimal and complete trajectories. This paper extends the search-based planning approach to address three important scenarios for MAVs navigation: (i) planning safe trajectories in the presence of motion uncertainty; (ii) planning with constraints on field-of-view and (iii) planning in dynamic environments. We show that these problems can be solved effectively and efficiently using the proposed search-based planning with motion primitives.

Connected through hyperedge HE-1 (induced by source paper "decentralized multi target tracking in urban environments overview and challenges"):
  with members:
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "sensor selection for target tracking in wireless sensor networks with uncertainty"
    - "the cauchy schwarz divergence for poisson point processes"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "cooperative multi target localization with noisy sensors"
    - "point based pomdp algorithms improved analysis and implementation"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-2 (induced by source paper "multi agent path finding with capacity constraints"):
  with members:
    - "multi agent path finding with deadlines"
    - "lazy modeling of variants of token swapping problem and multi agent path finding through combination of satisfiability modulo theories and conflict based search"
    - "overview generalizations of multi agent path finding to real world scenarios"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"
    - "exploiting subgraph structure in multi robot path planning"

Connected through hyperedge HE-3 (induced by source paper "inverse optimal planning for air traffic control"):
  with members:
    - "search based motion planning for aggressive flight in se 3"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "maximum entropy deep inverse reinforcement learning"

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)