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: "physics of the shannon limits"
  Abstract: We provide a simple physical interpretation, in the context of the second law of thermodynamics, to the information inequality (a.k.a. the Gibbs' inequality, which is also equivalent to the log-sum inequality), asserting that the relative entropy between two probability distributions cannot be negative. Since this inequality stands at the basis of the data processing theorem (DPT), and the DPT in turn is at the heart of most, if not all, proofs of converse theorems in Shannon theory, it is observed that conceptually, the roots of fundamental limits of Information Theory can actually be attributed to the laws of physics, in particular, to the second law of thermodynamics, and at least indirectly, also to the law of energy conservation. By the same token, in the other direction: one can view the second law as stemming from information-theoretic principles.

Connected through hyperedge HE-1 (induced by source paper "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"):
  with members:
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "vector precoding for gaussian mimo broadcast channels impact of replica symmetry breaking"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "information theoretic bounds for compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"

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)