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: "algorithmic data analytics small data matters and correlation versus causation"
  Abstract: This is a review of aspects of the theory of algorithmic information that may contribute to a framework for formulating questions related to complex, highly unpredictable systems. We start by contrasting Shannon entropy and Kolmogorov-Chaitin complexity, which epitomize correlation and causation respectively, and then surveying classical results from algorithmic complexity and algorithmic probability, highlighting their deep connection to the study of automata frequency distributions. We end by showing that though long-range algorithmic prediction models for economic and biological systems may require infinite computation, locally approximated short-range estimations are possible, thereby demonstrating how small data can deliver important insights into important features of complex “Big Data”.

Connected through hyperedge HE-1 (induced by source paper "parameter free network sparsification and data reduction by minimal algorithmic information loss"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "a model of internet topology using k shell decomposition"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-2 (induced by source paper "an algorithmic refinement of maxent induces a thermodynamic like behaviour in the reprogrammability of generative mechanisms"):
  with members:
    - "parameter free network sparsification and data reduction by minimal algorithmic information loss"
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "shannon information and kolmogorov complexity"

Connected through hyperedge HE-3 (induced by source paper "paths to unconventional computing causality in complexity"):
  with members:
    - "asymptotic intrinsic universality and natural reprogrammability by behavioural emulation"
    - "causality information and biological computation an algorithmic software approach to life disease and the immune system"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a computable measure of algorithmic probability by finite approximations"):
  with members:
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "algorithmic complexity for short binary strings applied to psychology a primer"
    - "algorithmic complexity for psychology a user friendly implementation of the coding theorem method"
    - "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"
    - "correspondence and independence of numerical evaluations of algorithmic information measures"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"

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.CE(Computational Engineering, Finance, and Science)