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: "distributed smc phd fusion for partial arithmetic average consensus"
  Abstract: We propose an average consensus approach for distributed SMC-PHD (sequential Monte Carlo-probability hypothesis density) fusion, in which local filters extract Gaussian mixtures (GMs) from their respective particle posteriors, share them (iteratively) with their neighbors and finally use the disseminated GM to update the particle weight. There are two distinguishable features of our approach compared to exiting approaches. First, a computationally efficient particles-to-GM (P2GM) conversion scheme is developed based on the unique structure of the SMC-PHD updater in which the particle weight can be exactly decomposed with regard to the measurements and misdetection. Only significant components of higher weight are utilized for parameterization. The consensus, conditioned on partial information dissemination over the network, is called "partial consensus". Second, importance sampling (IS) is employed to re-weight the local particles for integrating the received GM information, while the states of the particles remain unchanged. By this, the local prior PHD and likelihood calculation can be carried out in parallel to the dissemination \& fusion procedure. To assess the effectiveness of the proposed P2GM parameterization approach and IS approach, two relevant yet new distributed SMC-PHD fusion protocols are introduced for comparison. One uses the same P2GM conversion and GM dissemination schemes as our approach but local particles are regenerated from the disseminated GMs at each filtering iteration - in place of the IS approach. This performs similar to our IS approach (as expected) but prevents any parallelization as addressed above. The other is disseminating the particles between neighbors - in place of the P2GM conversion. This avoids parameterization but is communicatively costly. The state-of-the-art exponential mixture density approach is also realized for comparison.

Connected through hyperedge HE-1 (induced by source paper "multiobject fusion with minimum information loss"):
  with members:
    - "partial consensus and conservative fusion of gaussian mixtures for distributed phd fusion"
    - "second order statistics analysis and comparison between arithmetic and geometric average fusion application to multi sensor target tracking"
    - "robust distributed fusion with labeled random finite sets"
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "the cauchy schwarz divergence for poisson point processes"
    - "consensus labeled random finite set filtering for distributed multi object tracking"

Connected through hyperedge HE-2 (induced by source paper "partial consensus and conservative fusion of gaussian mixtures for distributed phd fusion"):
  with members:
    - "robust distributed fusion with labeled random finite sets"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "sigma point belief propagation"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "second order statistics analysis and comparison between arithmetic and geometric average fusion application to multi sensor target tracking"):
  with members:
    - "partial consensus and conservative fusion of gaussian mixtures for distributed phd fusion"
    - "a distributed particle phd filter with arithmetic average phd fusion"
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "chernoff information of exponential families"
    - "gossip algorithms for distributed signal processing"

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)