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: "causality information and biological computation an algorithmic software approach to life disease and the immune system"
  Abstract: Information and computation have transformed the way we look at the world beyond statistical correlations, the way we can perform experiments through simulations, and the way we can test these hypotheses. In Turing's seminal paper on the question of machine intelligence (Turing, 1950), his approach consisted in taking a computer to be a black box and evaluating it by way of what one could say about its apparent behaviour. The approach can be seen as a digital version of a  cogito ergo sum  dictum, acknowledging that one can be certain only of one's own intelligence but not of the intelligence of anyone or anything else, including machines. This may indeed amount to a kind of solipsism, but in practice it suggests tools that can be generalised. This is how in reality we approach most areas of science and technology, not just out of pragmatic considerations but for a fundamental reason. Turing, likeGodel before him (Godel, 1958), showed that systems such as arithmetic that have a certain minimal mathematical power to express something can produce outputs of an infinitely complicated nature (Turing, 1936). Indeed, this means that while one can design software, only trivial programs can be fully understood analytically to the point where one is able to make certain predictions about them. In other words, only by running software can one verify its computational properties, such as whether or not it will crash. These fundamental results deriving from both Godel's incompleteness theorem and Turing's halting problem point in the direction that Turing himself took in his ‘imitation game’, which we now identify as the  Turing test .  Turing's halting problem implies that one cannot in general prove that a machine will ever halt or that a certain configuration will be reached on halting; therefore, one has to proceed by testing and only by testing. These fundamental theorems and results imply that testing is unavoidable; even under optimal conditions there are fundamental limits to the knowledge one can obtain simply by looking at a system, or even by examining its source code, that is, its ultimate causes. Even with a knowledge of all the causes driving a system's evolution, there are strong limits to the kind of understanding of it that we can attain.

Connected through hyperedge HE-1 (induced by source paper "paths to unconventional computing causality in complexity"):
  with members:
    - "algorithmic data analytics small data matters and correlation versus causation"
    - "asymptotic intrinsic universality and natural reprogrammability by behavioural emulation"
    - "cross boundary behavioural reprogrammability of cellular automata from emulation networks"
    - "natural scene statistics mediate the perception of image complexity"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "what is nature like computation a behavioural approach and a notion of programmability"
    - "a behavioural foundation for natural computing and a programmability test"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"

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)