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: "particle swarm optimization a survey of historical and recent developments with hybridization perspectives"
  Abstract: Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Connected through hyperedge HE-1 (induced by source paper "data driven optimization of public transit schedule"):
  with members:
    - "a review of deep learning with special emphasis on architectures applications and recent trends"
    - "data clustering using a hybrid of fuzzy c means and quantum behaved particle swarm optimization"

Connected through hyperedge HE-2 (induced by source paper "chaotic quantum double delta swarm algorithm using chebyshev maps theoretical foundations performance analyses and convergence issues"):
  with members:
    - "qdds a novel quantum swarm algorithm inspired by a double dirac delta potential"
    - "data clustering using a hybrid of fuzzy c means and quantum behaved particle swarm optimization"

Connected through hyperedge HE-3 (induced by source paper "mechanisms for integrated feature normalization and remaining useful life estimation using lstms applied to hard disks"):
  with members:
    - "qdds a novel quantum swarm algorithm inspired by a double dirac delta potential"
    - "data clustering using a hybrid of fuzzy c means and quantum behaved particle swarm optimization"
    - "predicting remaining useful life using time series embeddings based on recurrent neural networks"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "learning to track on the fly using a particle filter with annealed weighted qpso modeled after a singular dirac delta potential"):
  with members:
    - "data clustering using a hybrid of fuzzy c means and quantum behaved particle swarm optimization"
    - "learning to track at 100 fps with deep regression networks"

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.NE(Neural and Evolutionary Computing)