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: "the tsallis entropy and the shannon entropy of a universal probability"
  Abstract: We study the properties of Tsallis entropy and Shannon entropy from the point of view of algorithmic randomness. In algorithmic information theory, there are two equivalent ways to define the program-size complexity K(s) of a given finite binary string s. In the standard way, K(s) is defined as the length of the shortest input string for the universal self-delimiting Turing machine to output s. In the other way, the so-called universal probability m is introduced first, and then K(s) is defined as -log2 m(s) without reference to the concept of program-size. In this paper, we investigate the properties of the Shannon entropy, the power sum, and the Tsallis entropy of a universal probability by means of the notion of program-size complexity. We determine the convergence or divergence of each of these three quantities, and evaluate its degree of randomness if it converges.

Connected through hyperedge HE-1 (induced by source paper "a statistical mechanical interpretation of algorithmic information theory"):
  with members:
    - "fixed point theorems on partial randomness"
    - "a statistical mechanical interpretation of algorithmic information theory iii composite systems and fixed points"
    - "a statistical mechanical interpretation of instantaneous codes"
    - "natural halting probabilities partial randomness and zeta functions"

Connected through hyperedge HE-2 (induced by source paper "finite biased teaching with infinite concept classes"):
  with members:
    - "neural programming by example"
    - "machine teaching for bayesian learners in the exponential family"

Connected through hyperedge HE-3 (induced by source paper "fixed point theorems on partial randomness"):
  with members:
    - "a statistical mechanical interpretation of algorithmic information theory"
    - "a statistical mechanical interpretation of instantaneous codes"
    - "natural halting probabilities partial randomness and zeta functions"

Connected through hyperedge HE-4 (induced by source paper "equivalent characterizations of partial randomness for a recursively enumerable real"):
  with members:
    - "a statistical mechanical interpretation of algorithmic information theory"
    - "natural halting probabilities partial randomness and zeta functions"

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)