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: "genetic algorithm seeding of idiotypic networks for mobile robot navigation"
  Abstract: Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jernes idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, pre-engineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

Connected through hyperedge HE-1 (induced by source paper "a genetic algorithm for autonomous navigation in partially observable domain"):
  with members:
    - "intelligent bug algorithm iba a novel strategy to navigate mobile robots autonomously"

Connected through hyperedge HE-2 (induced by source paper "immuno inspired robotic applications a review"):
  with members:
    - "two timescale learning using idiotypic behaviour mediation for a navigating mobile robot"
    - "the deterministic dendritic cell algorithm"
    - "the application of a dendritic cell algorithm to a robotic classifier"
    - "an idiotypic immune network as a short term learning architecture for mobile robots"
    - "articulation and clarification of the dendritic cell algorithm"
    - "idiotypic immune networks in mobile robot control"

Connected through hyperedge HE-3 (induced by source paper "the transfer of evolved artificial immune system behaviours between small and large scale robotic platforms"):
  with members:
    - "an idiotypic immune network as a short term learning architecture for mobile robots"
    - "idiotypic immune networks in mobile robot control"
    - "a recommender system based on idiotypic artificial immune networks"
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-4 (induced by source paper "two timescale learning using idiotypic behaviour mediation for a navigating mobile robot"):
  with members:
    - "an idiotypic immune network as a short term learning architecture for mobile robots"
    - "idiotypic immune networks in mobile robot control"
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-5 (induced by source paper "an idiotypic immune network as a short term learning architecture for mobile robots"):
  with members:
    - "idiotypic immune networks in mobile robot control"
    - "webotstm professional mobile robot simulation"

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)