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: "on empirical cumulant generating functions of code lengths for individual sequences"
  Abstract: We consider the problem of lossless compression of individual sequences using finite–state (FS) machines, from the perspective of the best achievable empirical cumulant generating function (CGF) of the code length, i.e., the normalized logarithm of the empirical average of the exponentiated code length. Since the probabilistic CGF is minimized in terms of the Renyi entropy of the source, one of the motivations of this study is to derive an individual–sequence analogue of the Renyi entropy, in the same way that the FS compressibility is the individual–sequence counterpart of the Shannon entropy. We consider the CGF of the code-length both from the perspective of fixed–to–variable (F-V) length coding and the perspective of variable–to–variable (V-V) length coding, where the latter turns out to yield a better result, that coincides with the FS compressibility. We also extend our results to compression with side information, available at both the encoder and decoder. In this case, the V–V version no longer coincides with the FS compressibility, but results in a different complexity measure. Index Terms Individual sequences, compressibility, finite–state machines, cumulant generating function, Renyi entropy, Lempel–Ziv algorithm.

Connected through hyperedge HE-1 (induced by source paper "improved bounds on lossless source coding and guessing moments via r e nyi measures"):
  with members:
    - "distributed task encoding"
    - "reducing guesswork via an unreliable oracle"
    - "guessing attacks on distributed storage systems"
    - "sharp bounds on arimoto s conditional r e nyi entropies between two distinct orders"
    - "f divergence inequalities"
    - "on the smooth renyi entropy and variable length source coding allowing errors"
    - "encoding tasks and renyi entropy"
    - "renyi divergence and kullback leibler divergence"

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)