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: "an idiotypic immune network as a short term learning architecture for mobile robots"
  Abstract: A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability

Connected through hyperedge HE-1 (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"
    - "articulation and clarification of the dendritic cell algorithm"
    - "genetic algorithm seeding of idiotypic networks for mobile robot navigation"
    - "idiotypic immune networks in mobile robot control"

Connected through hyperedge HE-2 (induced by source paper "the use of probabilistic systems to mimic the behaviour of idiotypic ais robot controllers"):
  with members:
    - "two timescale learning using idiotypic behaviour mediation for a navigating mobile robot"
    - "the transfer of evolved artificial immune system behaviours between small and large scale robotic platforms"
    - "idiotypic immune networks in mobile robot control"
    - "reinforcement learning a survey"

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:
    - "genetic algorithm seeding of idiotypic networks for mobile robot navigation"
    - "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:
    - "genetic algorithm seeding of idiotypic networks for mobile robot navigation"
    - "idiotypic immune networks in mobile robot control"
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-5 (induced by source paper "mimicking the behaviour of idiotypic ais robot controllers using probabilistic systems"):
  with members:
    - "idiotypic immune networks in mobile robot control"
    - "reinforcement learning a survey"

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.AI(Artificial Intelligence)