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: "evolutionary multitasking for single objective continuous optimization benchmark problems performance metric and baseline results"
  Abstract: In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTMOO research.

Connected through hyperedge HE-1 (induced by source paper "quality diversity for multi task optimization"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "procedural content generation through quality diversity"
    - "reset free trial and error learning for robot damage recovery"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"

Connected through hyperedge HE-2 (induced by source paper "simultaneously evolving deep reinforcement learning models using multifactorial optimization"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "deep reinforcement learning a brief survey"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "genetic transfer or population diversification deciphering the secret ingredients of evolutionary multitask optimization"
    - "how transferable are features in deep neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "a two stage adaptive knowledge transfer evolutionary multi tasking based on population distribution for multi many objective optimization"):
  with members:
    - "igd indicator based evolutionary algorithm for many objective optimization problems"

Connected through hyperedge HE-4 (induced by source paper "multi tasking genetic algorithm mtga for fuzzy system optimization"):
  with members:
    - "affect estimation in 3d space using multi task active learning for regression"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

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)