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: "hybrid poisson and multi bernoulli filters"
  Abstract: The probability hypothesis density (PHD) and multi-target multi-Bernoulli (MeMBer) filters are two leading algorithms that have emerged from random finite sets (RFS). In this paper we study a method which combines these two approaches. Our work is motivated by a sister paper, which proves that the full Bayes RFS filter naturally incorporates a Poisson component representing targets that have never been detected, and a linear combination of multi-Bernoulli components representing targets under track. Here we demonstrate the benefit (in speed of track initiation) that maintenance of a Poisson component of undetected targets provides. Subsequently, we propose a method of recycling, which projects Bernoulli components with a low probability of existence onto the Poisson component (as opposed to deleting them). We show that this allows us to achieve similar tracking performance using a fraction of the number of Bernoulli components (i.e., tracks).

Connected through hyperedge HE-1 (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"
    - "approximate evaluation of marginal association probabilities with belief propagation"

Connected through hyperedge HE-2 (induced by source paper "distributed fusion with multi bernoulli filter based on generalized covariance intersection"):
  with members:
    - "distributed fusion of labeled multi object densities via label spaces matching"
    - "multi bernoulli sensor control via minimization of expected estimation errors"
    - "consensus labeled random finite set filtering for distributed multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"):
  with members:
    - "an efficient variational approximation of the best fitting multi bernoulli filter"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "the bethe permanent of a nonnegative matrix"
    - "graphical model approximations of random finite set filters"
    - "approximating the permanent with belief propagation"
    - "belief propagation and beyond for particle tracking"

Connected through hyperedge HE-4 (induced by source paper "an efficient variational approximation of the best fitting multi bernoulli filter"):
  with members:
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "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.SY(Systems and Control)