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: "generic behaviour similarity measures for evolutionary swarm robotics"
  Abstract: Novelty search has shown to be a promising approach for the evolution of controllers for swarms of robots. In existing studies, however, the experimenter had to craft a task-specific behaviour similarity measure. The reliance on hand-crafted similarity measures places an additional burden to the experimenter and introduces a bias in the evolutionary process. In this paper, we propose and compare two generic behaviour similarity measures: combined state count and sampled average state. The proposed measures are based on the values of sensors and effectors recorded for each individual robot of the swarm. The characterisation of the group-level behaviour is then obtained by combining the sensor-effector values from all the robots. We evaluate the proposed measures in an aggregation task and in a resource sharing task. We show that the generic measures match the performance of task-specific measures in terms of solution quality. Our results indicate that the proposed generic measures operate as effective behaviour similarity measures, and that it is possible to leverage the benefits of novelty search without having to craft task-specific similarity measures.

Connected through hyperedge HE-1 (induced by source paper "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"):
  with members:
    - "distributed prioritized experience replay"
    - "evolved policy gradients"
    - "rainbow combining improvements in deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "count based exploration with neural density models"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "discovering evolutionary stepping stones through behavior domination"):
  with members:
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "systematic derivation of behaviour characterisations in evolutionary robotics"
    - "evolvability is inevitable increasing evolvability without the pressure to adapt"

Connected through hyperedge HE-3 (induced by source paper "evolution of collective behaviors for a real swarm of aquatic surface robots"):
  with members:
    - "robots that can adapt like animals"
    - "evolution of swarm robotics systems with novelty search"

Connected through hyperedge HE-4 (induced by source paper "systematic derivation of behaviour characterisations in evolutionary robotics"):
  with members:
    - "evolution of swarm robotics systems with novelty search"

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)