human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lossless source codes for a class described by variational distance and optimal merging algorithm"):
  Abstract: AbstractThis paper considers lossless uniquely decodable source codes for a class of distributions describedby a ball with respect to the total variational distance, centered at a nominal distribution with a givenradius. The coding problem is formulated using minimax techniques, in which the maximum of theaverage codeword length over the class of distributions is minimized subject to Kraft inequality. Firstly,the maximization over the class of distributions is characterized resulting in an equivalent pay-off.consisting of the maximum and minimum codeword length and the average codeword length withrespect to the nominal distribution. Secondly, an algorithm is introduced which computes the optimalweight vector as a function of the class radius. Finally, the optimal codeword length vector is found asa function of the weight vector. I. I NTRODUCTION Lossless source codes for known probability distributions are investigated for several pay-offs,such as the average codeword length [1] and the average redundancy of the codeword length,the average of an exponential function of the codeword length [2]–[4], and the average of anexponential function of the redundancy of the codeword length [4]–[6]. For the average codewordlength pay-off the average redundancy is bounded below by zero and above by one. On the otherhand, if the true probability distribution of the source is unknown and the code is designed solely

Includes members:
  Member "tight bounds on minimum maximum pointwise redundancy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variable length lossless coding for variational distance class an optimal merging algorithm")
  Member "optimal prefix codes for infinite alphabets with nonlinear costs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal merging algorithms for lossless codes with generalized criteria")
  Member "a general framework for codes involving redundancy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variable length lossless coding for variational distance class an optimal merging algorithm")

Question: Please tell me which class the source paper 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)