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: "multiple scan data association by convex variational inference"
  Abstract: Data association, the reasoning over correspondence between targets and measurements, is a problem of fundamental importance in target tracking. Recently, belief propagation (BP) has emerged as a promising method for estimating the marginal probabilities of measurement to target association, providing fast, accurate estimates. The excellent performance of BP in the particular formulation used may be attributed to the convexity of the underlying free energy which it implicitly optimises. This paper studies multiple scan data association problems, i.e., problems that reason over correspondence between targets and several sets of measurements, which may correspond to different sensors or different time steps. We find that the multiple scan extension of the single scan BP formulation is non-convex and demonstrate the undesirable behaviour that can result. A convex free energy is constructed using the recently proposed fractional free energy (FFE). A convergent, BP-like algorithm is provided for the single scan FFE, and employed in optimising the multiple scan free energy using primal-dual coordinate ascent. Finally, based on a variational interpretation of joint probabilistic data association (JPDA), we develop a sequential variant of the algorithm that is similar to JPDA, but retains consistency constraints from prior scans. The performance of the proposed methods is demonstrated on a bearings only target localisation problem.

Connected through hyperedge HE-1 (induced by source paper "multisensor poisson multi bernoulli filter for joint target sensor state tracking"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "an efficient variational approximation of the best fitting multi bernoulli filter"

Connected through hyperedge HE-2 (induced by source paper "multisensor poisson multi bernoulli filtering with uncertain sensor states"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "graphical model approximations of random finite set filters"

Connected through hyperedge HE-3 (induced by source paper "joint target detection and tracking in multipath environment a variational bayesian approach"):
  with members:
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "extended object tracking introduction overview and applications"
    - "approximate evaluation of marginal association probabilities with belief propagation"

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.AI(Artificial Intelligence)