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: "potential game based non myopic sensor network planning for multi target tracking"
  Abstract: This paper presents a potential game-based method for the non-myopic planning of mobile sensor networks in the context of target tracking. The planning objective is to select the sequence of sensing points over more than one future time step to maximize information about the target states. This multi-step lookahead scheme aims to overcome getting trapped at local information maximum when there are gaps in the sensing coverage due to constraints of the sensor platform mobility or limitations in sensing capabilities. However, long-term planning becomes computationally intractable as the length of planning horizon increases. This paper develops a game-theoretic approach to address the computational challenges. The main contributions of this paper are twofold: 1) to formulate a non-myopic planning problem for tracking multiple targets in a potential game, the size of which increases linearly as the number of planning steps and 2) to design a learning algorithm exploiting the joint strategy fictitious play and dynamic programming, which overcomes the gaps in sensing coverage. The numerical examples of multi-target tracking demonstrate that the proposed method gives a better estimation performance than myopic planning and is computationally tractable.

Connected through hyperedge HE-1 (induced by source paper "a distributed admm approach to non myopic path planning for multi target tracking"):
  with members:
    - "fast admm algorithm for distributed optimization with adaptive penalty"
    - "a message passing algorithm for multi agent trajectory planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "a distributed admm approach to informative trajectory planning for multi target tracking"):
  with members:
    - "an improved three weight message passing algorithm"

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.SY(Systems and Control)