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: "approximate evaluation of marginal association probabilities with belief propagation"
  Abstract: Data association, the problem of reasoning over correspondence between targets and measurements, is a fundamental problem in tracking. This paper presents a graphical model formulation of data association and applies an approximate inference method, belief propagation (BP), to obtain estimates of marginal association probabilities. We prove that BP is guaranteed to converge, and bound the number of iterations necessary. Experiments reveal a favourable comparison to prior methods in terms of accuracy and computational complexity.

Connected through hyperedge HE-1 (induced by source paper "a belief propagation algorithm for multipath based slam"):
  with members:
    - "generalized optimal sub pattern assignment metric"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "cooperative simultaneous localization and synchronization in mobile agent networks"
    - "variational bayesian inference of line spectra"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "evaluation of position related information in multipath components for indoor positioning"

Connected through hyperedge HE-2 (induced by source paper "machine learning methods for solving assignment problems in multi target tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "combined image and world space tracking in traffic scenes"
    - "graph embedding techniques applications and performance a survey"
    - "carla an open urban driving simulator"
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "a hybrid data association framework for robust online multi object tracking"
    - "deep reinforcement learning a brief survey"
    - "fusion of head and full body detectors for multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "joint mapping and calibration via differentiable sensor fusion"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "particle filter networks with application to visual localization"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "optnet differentiable optimization as a layer in neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "measurement wise occlusion in multi object tracking"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "generalized optimal sub pattern assignment metric"
    - "fully convolutional neural networks for dynamic object detection in grid maps"
    - "mot16 a benchmark for multi object tracking"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "hybrid poisson and multi bernoulli filters"

Connected through hyperedge HE-5 (induced by source paper "a scalable belief propagation algorithm for radio signal based slam"):
  with members:
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "variational bayesian inference of line spectra"
    - "evaluation of position related information in multipath components for indoor positioning"

Connected through hyperedge HE-6 (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"
    - "multiple scan data association by convex variational inference"
    - "graphical model approximations of random finite set filters"

Connected through hyperedge HE-7 (induced by source paper "a scalable algorithm for tracking an unknown number of targets using multiple sensors"):
  with members:
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-8 (induced by source paper "generalized optimal sub pattern assignment metric"):
  with members:
    - "an efficient variational approximation of the best fitting multi bernoulli filter"

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)