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: "renyi to renyi source coding under siege"
  Abstract: A novel lossless source coding paradigm applies to problems of unreliable lossless channels with low bit rates, in which a vital message needs to be transmitted prior to termination of communications. This paradigm can be applied to Alfred Renyi's secondhand account of an ancient siege in which a spy was sent to scout the enemy but was captured. After escaping, the spy returned to his base in no condition to speak and unable to write. His commander asked him questions that he could answer by nodding or shaking his head, and the fortress was defended with this information. Renyi told this story with reference to prefix coding, but maximizing probability of survival in the siege scenario is distinct from yet related to the traditional source coding objective of minimizing expected codeword length. Rather than finding a code minimizing expected codeword length $\sum_{i=1}^n p(i) l(i)$, the siege problem involves maximizing $\sum_{i=1}^n p(i) \theta^{l(i)}$ for a known $\theta \in (0,1)$. When there are no restrictions on codewords, this problem can be solve using a known generalization of Huffman coding. The optimal solution has coding bounds which are functions of Renyi entropy; in addition to known bounds, new bounds are derived here. The alphabetically constrained version of this problem has applications in search trees and diagnostic testing. A novel dynamic programming algorithm -- based upon the oldest known algorithm for the traditional alphabetic problem -- optimizes this problem in $O(n^3)$ time and $O(n^2)$ space, whereas two novel approximation algorithms can find a suboptimal solution faster: one in linear time, the other in $O(n \log n)$. Coding bounds for the alphabetic version of this problem are also presented.

Connected through hyperedge HE-1 (induced by source paper "tight bounds on minimum maximum pointwise redundancy"):
  with members:
    - "optimal prefix codes for infinite alphabets with nonlinear costs"
    - "a general framework for codes involving redundancy minimization"

Connected through hyperedge HE-2 (induced by source paper "alphabetic coding with exponential costs"):
  with members:
    - "optimal prefix codes for infinite alphabets with nonlinear costs"
    - "a general framework for codes involving redundancy minimization"
    - "source coding for quasiarithmetic penalties"

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)