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 business ecosystems natural science paradigms"
  Abstract: A primary motivation for research in Digital Ecosystems is the desire to exploit the self-organising properties of natural ecosystems. Ecosystems arc thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystem research. Here, we introduce how biological properties contribute to the self-organising features of natural ecosystems. These properties include populations of evolving agents, a complex dynamic environment, and spatial distributions which generate local interactions. The potential for exploiting these properties in artificial systems is then considered.

Connected through hyperedge HE-1 (induced by source paper "crowds for clouds recent trends in humanities research infrastructures"):
  with members:
    - "the past and the future of holocaust research from disparate sources to an integrated european holocaust research infrastructure"

Connected through hyperedge HE-2 (induced by source paper "the computing of digital ecosystems"):
  with members:
    - "complex adaptive digital ecosystems"
    - "the abc of digital business ecosystems"
    - "digital ecosystems self organisation of evolving agent populations"
    - "biology of applied digital ecosystems"
    - "digital ecosystems optimisation by a distributed intelligence"
    - "digital ecosystems evolving service oriented architectures"
    - "creating a digital ecosystem service oriented architectures with distributed evolutionary computing"

Connected through hyperedge HE-3 (induced by source paper "digital ecosystems ecosystem oriented architectures"):
  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"
    - "biology of applied digital ecosystems"
    - "digital ecosystems evolving service oriented architectures"
    - "avoiding the bloat with stochastic grammar based genetic programming"

Connected through hyperedge HE-4 (induced by source paper "computing of applied digital ecosystems"):
  with members:
    - "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-5 (induced by source paper "digital ecosystems self organisation of evolving agent populations"):
  with members:
    - "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-6 (induced by source paper "digital ecosystems optimisation by a distributed intelligence"):
  with members:
    - "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)