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: "credit assignment in adaptive evolutionary algorithms"
  Abstract: In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance measurements, distributing credit for performance, and the statistical interpretation of this credit, a new adaptive method is developed and shown to outperform a variety of adaptive and non-adaptive competitors.

Connected through hyperedge HE-1 (induced by source paper "neuro evolutionary frameworks for generalized learning agents"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "meta learning by the baldwin effect"
    - "investigating human priors for playing video games"
    - "a self replication basis for designing complex agents"
    - "hdltex hierarchical deep learning for text classification"
    - "a survey on multi task learning"

Connected through hyperedge HE-2 (induced by source paper "multi robot organisms state of the art"):
  with members:
    - "challenges and opportunities of evolutionary robotics"

Connected through hyperedge HE-3 (induced by source paper "adaptation and self organization in evolutionary algorithms"):
  with members:
    - "the self organization of interaction networks for nature inspired optimization"
    - "revisiting evolutionary algorithms with on the fly population size adjustment"
    - "use of statistical outlier detection method in adaptive evolutionary algorithms"
    - "competitive coevolution through evolutionary complexification"
    - "benchmarking optimization software with performance profiles"

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)