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: "data clustering using a hybrid of fuzzy c means and quantum behaved particle swarm optimization"
  Abstract: Fuzzy clustering has become a widely used data mining technique and plays an important role in grouping, traversing and selectively using data for user specified applications. The deterministic Fuzzy C-Means (FCM) algorithm may result in suboptimal solutions when applied to multidimensional data in real-world, time-constrained problems. In this paper the Quantum-behaved Particle Swarm Optimization (QPSO) with a fully connected topology is coupled with the Fuzzy C-Means Clustering algorithm and is tested on a suite of datasets from the UCI Machine Learning Repository. The global search ability of the QPSO algorithm helps in avoiding stagnation in local optima while the soft clustering approach of FCM helps to partition data based on membership probabilities. Clustering performance indices such as F-Measure, Accuracy, Quantization Error, Intercluster and Intracluster distances are reported for competitive techniques such as PSO K-Means, QPSO K-Means and QPSO FCM over all datasets considered. Experimental results indicate that QPSO FCM provides comparable and in most cases superior results when compared to the others.

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"
    - "particle swarm optimization a survey of historical and recent developments with hybridization perspectives"

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"
    - "particle swarm optimization a survey of historical and recent developments with hybridization perspectives"

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"
    - "particle swarm optimization a survey of historical and recent developments with hybridization perspectives"
    - "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:
    - "particle swarm optimization a survey of historical and recent developments with hybridization perspectives"
    - "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)