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: "an improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems"
  Abstract: Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's t-test, the Holm's test, and the Friedman test. We have also compared the convergence behavior shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

Connected through hyperedge HE-1 (induced by source paper "from data to actions in intelligent transportation systems a prescription of functional requirements for model actionability"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "predicting transportation modes of gps trajectories using feature engineering and noise removal"
    - "explanation methods in deep learning users values concerns and challenges"
    - "transferable pedestrian motion prediction models at intersections"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "understanding traffic density from large scale web camera data"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"

Connected through hyperedge HE-2 (induced by source paper "nature inspired optimization algorithms challenges and open problems"):
  with members:
    - "self adaptive decision making mechanisms to balance the execution of multiple tasks for a multi robots team"
    - "swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks"
    - "new directional bat algorithm for continuous optimization problems"
    - "a discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy"
    - "a comprehensive review of firefly algorithms"
    - "a literature survey of benchmark functions for global optimisation problems"
    - "multi objective flower algorithm for optimization"
    - "bat algorithm literature review and applications"

Connected through hyperedge HE-3 (induced by source paper "neighborhood information based probabilistic algorithm for network disintegration"):
  with members:
    - "variational community partition with novel network structure centrality prior"
    - "a discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy"

Connected through hyperedge HE-4 (induced by source paper "swarm intelligence past present and future"):
  with members:
    - "a discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy"
    - "random key cuckoo search for the travelling salesman problem"
    - "a comprehensive review of firefly algorithms"
    - "bat algorithm literature review and applications"

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)