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: "3 d reciprocal collision avoidance on physical quadrotor helicopters with on board sensing for relative positioning"
  Abstract: In this paper, we present an implementation of 3-D reciprocal collision avoidance on real quadrotor helicopters where each quadrotor senses the relative position and velocity of other quadrotors using an on-board camera. We show that using our approach, quadrotors are able to successfully avoid pairwise collisions in GPS and motion-capture denied environments, without communication between the quadrotors, and even when human operators deliberately attempt to induce collisions. To our knowledge, this is the first time that reciprocal collision avoidance has been successfully implemented on real robots where each agent independently observes the others using on-board sensors. We theoretically analyze the response of the collision-avoidance algorithm to the violated assumptions by the use of real robots. We quantitatively analyze our experimental results. A particularly striking observation is that at times the quadrotors exhibit "reciprocal dance" behavior, which is also observed when humans move past each other in constrained environments. This seems to be the result of sensing uncertainty, which causes both robots involved to have a different belief about the relative positions and velocities and, as a result, choose the same side on which to pass.

Connected through hyperedge HE-1 (induced by source paper "robust target relative localization with ultra wideband ranging and communication"):
  with members:
    - "correlation flow robust optical flow using kernel cross correlators"
    - "kernel cross correlator"
    - "ultra wideband aided fast localization and mapping system"

Connected through hyperedge HE-2 (induced by source paper "on board communication based relative localization for collision avoidance in micro air vehicle teams"):
  with members:
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"
    - "local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones"
    - "optical flow based self supervised learning of obstacle appearance applied to mav landing"
    - "outdoor flocking and formation flight with autonomous aerial robots"

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)