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: "low algorithmic complexity entropy deceiving graphs"
  Abstract: In estimating the complexity of objects, in particular of graphs, it is common practice to rely on graph- and information-theoretic measures. Here, using integer sequences with properties such as Borel normality, we explain how these measures are not independent of the way in which an object, such as a graph, can be described or observed. From observations that can reconstruct the same graph and are therefore essentially translations of the same description, we will see that when applying a computable measure such as Shannon Entropy, not only is it necessary to pre-select a feature of interest where there is one, and to make an arbitrary selection where there is not, but also that more general properties, such as the causal likelihood of a graph as a measure (opposed to randomness), can be largely misrepresented by computable measures such as Entropy and Entropy rate. We introduce recursive and non-recursive (uncomputable) graphs and graph constructions based on these integer sequences, whose different lossless descriptions have disparate Entropy values, thereby enabling the study and exploration of a measure's range of applications and demonstrating the weaknesses of computable measures of complexity.

Connected through hyperedge HE-1 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "a computable measure of algorithmic probability by finite approximations with an application to integer sequences"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "shannon information and kolmogorov complexity"

Connected through hyperedge HE-2 (induced by source paper "compression is comprehension and the unreasonable effectiveness of digital computation in the natural world"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "turing patterns with turing machines emergence and low level structure formation"
    - "on the kolmogorov chaitin complexity for short sequences"

Connected through hyperedge HE-3 (induced by source paper "ab initio algorithmic causal deconvolution of intertwined programs and networks by generative mechanism"):
  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"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "on the necessity of complexity"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"
    - "nonnegative decomposition of multivariate information"
    - "the generalized universal law of generalization"

Connected through hyperedge HE-4 (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"
    - "algorithmic data analytics small data matters and correlation versus causation"
    - "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-5 (induced by source paper "data dimension reduction and network sparsification based on minimal algorithmic information loss"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "correspondence and independence of numerical evaluations of algorithmic information measures"
    - "graph sparsification by effective resistances"
    - "a model of internet topology using k shell decomposition"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-6 (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"
    - "algorithmic data analytics small data matters and correlation versus causation"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "shannon information and kolmogorov complexity"

Connected through hyperedge HE-7 (induced by source paper "algorithmically random generalized graphs and their topological properties"):
  with members:
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "time centrality in dynamic complex networks"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"

Connected through hyperedge HE-8 (induced by source paper "symmetry and algorithmic complexity of polyominoes and polyhedral graphs"):
  with members:
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "structure induction by lossless graph compression"

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.IT(Information Theory)