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: "a multi hidden recurrent neural network with a modified grey wolf optimizer"
  Abstract: Identifying university students’ weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students’ outcomes. This proposed system would improve instruction by the faculty and enhance the students’ learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models

Connected through hyperedge HE-1 (induced by source paper "improved fitness dependent optimizer algorithm"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"
    - "a comprehensive study on pedestrians evacuation"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "dragonfly algorithm and its applications in applied science survey"
    - "a simulation model for pedestrian crowd evacuation based on various ai techniques"

Connected through hyperedge HE-2 (induced by source paper "cat swarm optimization algorithm a survey and performance evaluation"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "dragonfly algorithm and its applications in applied science survey"

Connected through hyperedge HE-3 (induced by source paper "a survey on dragonfly algorithm and its applications in engineering"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "a systematic and meta analysis survey of whale optimization algorithm"

Connected through hyperedge HE-4 (induced by source paper "a simulation model for pedestrian crowd evacuation based on various ai techniques"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "a comprehensive study on pedestrians evacuation"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "netlogo implementation of an evacuation scenario"
    - "modelling reactive and proactive behaviour in simulation a case study in a university organisation"

Connected through hyperedge HE-5 (induced by source paper "operational framework for recent advances in backtracking search optimisation algorithm a systematic review and performance evaluation"):
  with members:
    - "donkey and smuggler optimization algorithm a collaborative working approach to path finding"
    - "fitness dependent optimizer inspired by the bee swarming reproductive process"
    - "a systematic and meta analysis survey of whale optimization algorithm"
    - "adaptive evolutionary clustering"
    - "a literature survey of benchmark functions for global optimisation problems"

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)