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: "computing networks a general framework to contrast neural and swarm cognitions"
  Abstract: This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are used as examples of instantiations of CNs. The description of these architectures as CNs allows their comparison. Their differences and similarities allow the identification of properties that enable neural and swarm architectures to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. The relationship between multiple dynamical and functional scales with adaptation, cognition (of brains and swarms) and computation is discussed.

Connected through hyperedge HE-1 (induced by source paper "information and computation"):
  with members:
    - "learning social intelligence and the turing test why an out of the box turing machine will not pass the turing test"
    - "complexity and information measuring emergence self organization and homeostasis at multiple scales"
    - "the world as evolving information"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-2 (induced by source paper "living is information processing from molecules to global systems"):
  with members:
    - "complexity and information measuring emergence self organization and homeostasis at multiple scales"

Connected through hyperedge HE-3 (induced by source paper "complexity and information measuring emergence self organization and homeostasis at multiple scales"):
  with members:
    - "learning social intelligence and the turing test why an out of the box turing machine will not pass the turing test"
    - "the world as evolving information"
    - "numerical evaluation of algorithmic complexity for short strings a glance into the innermost structure of randomness"
    - "image characterization and classification by physical complexity"
    - "compression based investigation of the dynamical properties of cellular automata and other systems"
    - "on the kolmogorov chaitin complexity for short sequences"
    - "classification of random boolean networks"

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)