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: "idiotypic immune networks in mobile robot control"
  Abstract: Jerne's idiotypic-network theory postulates that the immune response involves interantibody stimulation and suppression, as well as matching to antigens. The theory has proved the most popular artificial immune system (AIS) model for incorporation into behavior-based robotics, but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with nonidiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a reinforcement-learning (RL)-based control system is described, and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels, and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

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"
    - "an idiotypic immune network as a short term learning architecture for mobile robots"
    - "articulation and clarification of the dendritic cell algorithm"
    - "genetic algorithm seeding of idiotypic networks for mobile robot navigation"

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"
    - "an idiotypic immune network as a short term learning architecture for mobile robots"
    - "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:
    - "an idiotypic immune network as a short term learning architecture for mobile robots"
    - "genetic algorithm seeding of idiotypic networks for mobile robot navigation"
    - "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"
    - "genetic algorithm seeding of idiotypic networks for mobile robot navigation"
    - "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:
    - "an idiotypic immune network as a short term learning architecture for mobile robots"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "an idiotypic immune network as a short term learning architecture for mobile robots"):
  with members:
    - "genetic algorithm seeding of idiotypic networks for mobile robot navigation"
    - "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)