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: "evolution of swarm robotics systems with novelty search"
  Abstract: Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task—aggregation, and a more challenging task—sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.

Connected through hyperedge HE-1 (induced by source paper "comparing and combining lexicase selection and novelty search"):
  with members:
    - "lexicase selection of specialists"
    - "epsilon lexicase selection for regression"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-2 (induced by source paper "swarm behaviour evolution via rule sharing and novelty search"):
  with members:
    - "discovering evolutionary stepping stones through behavior domination"
    - "lamarckian evolution and the baldwin effect in evolutionary neural networks"

Connected through hyperedge HE-3 (induced by source paper "enhanced optimization with composite objectives and novelty selection"):
  with members:
    - "discovering evolutionary stepping stones through behavior domination"
    - "sorting networks to the end and back again"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "the quest for optimal sorting networks efficient generation of two layer prefixes"

Connected through hyperedge HE-4 (induced by source paper "evolution of collective behaviors for a real swarm of aquatic surface robots"):
  with members:
    - "robots that can adapt like animals"
    - "generic behaviour similarity measures for evolutionary swarm robotics"

Connected through hyperedge HE-5 (induced by source paper "systematic derivation of behaviour characterisations in evolutionary robotics"):
  with members:
    - "generic behaviour similarity measures for evolutionary swarm robotics"

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)