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: "digital ecosystems optimisation by a distributed intelligence"
  Abstract: Can intelligence optimise Digital Ecosystems? How could a distributed intelligence interact with the ecosystem dynamics? Can the software components that are part of genetic selection be intelligent in themselves, as in an adaptive technology? We consider the effect of a distributed intelligence mechanism on the evolutionary and ecological dynamics of our Digital Ecosystem, which is the digital counterpart of a biological ecosystem for evolving software services in a distributed network. We investigate Neural Networks and Support Vector Machine for the learning based pattern recognition functionality of our distributed intelligence. Simulation results imply that the Digital Ecosystem performs better with the application of a distributed intelligence, marginally more effectively when powered by Support Vector Machine than Neural Networks, and suggest that it can contribute to optimising the operation of our Digital Ecosystem.

Connected through hyperedge HE-1 (induced by source paper "ecosystem oriented distributed evolutionary computing"):
  with members:
    - "self organisation of evolving agent populations in digital ecosystems"
    - "stability of evolving multiagent systems"
    - "the computing of digital ecosystems"
    - "digital ecosystems ecosystem oriented architectures"
    - "complex adaptive digital ecosystems"
    - "the abc of digital business ecosystems"
    - "digital ecosystems self organisation of evolving agent populations"
    - "digital ecosystems stability of evolving agent populations"

Connected through hyperedge HE-2 (induced by source paper "self organisation of evolving agent populations in digital ecosystems"):
  with members:
    - "the computing of digital ecosystems"
    - "complex adaptive digital ecosystems"
    - "the abc of digital business ecosystems"
    - "digital ecosystems self organisation of evolving agent populations"
    - "scalability and optimisation of a committee of agents using genetic algorithm"
    - "digital ecosystems stability of evolving agent populations"
    - "biology of applied digital ecosystems"
    - "digital ecosystems evolving service oriented architectures"

Connected through hyperedge HE-3 (induced by source paper "the computing of digital ecosystems"):
  with members:
    - "complex adaptive digital ecosystems"
    - "the abc of digital business ecosystems"
    - "digital business ecosystems natural science paradigms"
    - "digital ecosystems self organisation of evolving agent populations"
    - "biology of applied digital ecosystems"
    - "digital ecosystems evolving service oriented architectures"
    - "creating a digital ecosystem service oriented architectures with distributed evolutionary computing"

Connected through hyperedge HE-4 (induced by source paper "digital ecosystems stability of evolving agent populations"):
  with members:
    - "scalability and optimisation of a committee of agents using genetic algorithm"
    - "biology of applied digital ecosystems"
    - "digital ecosystems evolving service oriented architectures"
    - "creating a digital ecosystem service oriented architectures with distributed evolutionary computing"

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)