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: "adaptive trajectory estimation with power limited steering model under perturbation compensation"
  Abstract: Trajectory estimation under regional correlations is applied in numerous tasks like dynamic path planning, navigation and tracking. Many previous works get impressive results in the strictly controlled condition with accurate prior statistics and specific dynamic model for certain object. While in a more challenging situation without specific dynamic model and accurate prior statistics, the performance of these methods significantly declines. To estimate the trajectory without specific dynamic model, we propose a general model called the power-limited steering model (PLS), which is a natural combination of instantaneous power and instantaneous angular velocity. As a nonlinear model it needs less parameter to describe the switching of states compared with linear Markov process model. Then we derive the corresponding form in discrete time and compensate the nonlinear effect of perturbation with renormalization group. To overcome the biased prior statistics, the observations drift and linear growing computation in trajectory estimation, we propose the adaptive trajectory estimation (AdaTE) where the online updated statistics and the adaptive truncation time is applied. The experiment of typical trajectory estimation demonstrates that compared with EKF and UKF, AdaTE is more robust to the biased prior statistics and observation drift. Another task shows that with slight modification, AdaTE can be used in path planning that needs obstacle avoidance. Finally, we exhibit its potential application on trajectory optimization in visual tracking.

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.IT(Information Theory)