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: "quality diversity through surprise"
  Abstract: Quality diversity (QD) is a recent family of evolutionary search algorithms which focus on finding several well-performing ( quality ) yet different ( diversity ) solutions with the aim to maintain an appropriate balance between divergence and convergence during search. While QD has already delivered promising results in complex problems, the capacity of divergent search variants for QD remains largely unexplored. Inspired by the notion of  surprise  as an effective driver of divergent search and its orthogonal nature to  novelty  this paper investigates the impact of the former to QD performance. For that purpose we introduce three new QD algorithms which employ surprise as a diversity measure, either on its own or combined with novelty, and compare their performance against novelty search with local competition, the state of the art QD algorithm. The algorithms are tested in a robot navigation task across 60 highly deceptive mazes. Our findings suggest that allowing surprise and novelty to operate synergistically for divergence and in combination with local competition leads to QD algorithms of significantly higher efficiency, speed, and robustness.

Connected through hyperedge HE-1 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function"):
  with members:
    - "evaluating map elites on constrained optimization problems"
    - "precise runtime analysis for plateaus"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "quality and diversity optimization a unifying modular framework"
    - "illuminating search spaces by mapping elites"
    - "a tabu search path relinking algorithm to solve the job shop scheduling problem"
    - "novelty search in competitive coevolution"
    - "evaluating las vegas algorithms pitfalls and remedies"

Connected through hyperedge HE-2 (induced by source paper "procedural content generation through quality diversity"):
  with members:
    - "general video game rule generation"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "adaptive music composition for games"
    - "talakat bullet hell generation through constrained map elites"
    - "deceptive games"

Connected through hyperedge HE-3 (induced by source paper "exploration and exploitation in symbolic regression using quality diversity and evolutionary strategies algorithms"):
  with members:
    - "a greedy search tree heuristic for symbolic regression"
    - "analysing symbolic regression benchmarks under a meta learning approach"
    - "how noisy data affects geometric semantic genetic programming"
    - "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
    - "quality and diversity optimization a unifying modular framework"
    - "illuminating search spaces by mapping elites"

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)