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 "communication for omniscience"):
  Abstract: This paper considers the communication for omniscience (CO) problem: A set of users observe a discrete memoryless multiple source and want to recover the entire multiple source via noise-free broadcast communications. We study the problem of how to attain omniscience with the minimum sum-rate, the total number of communications, and determine a corresponding optimal rate vector. The results cover both asymptotic and non-asymptotic models where the transmission rates are real and integral, respectively. Based on the concepts of submodularity and Dilworth truncation, we formulate a maximization problem. The maximum is the highest Slepian-Wolf constraint over all multi-way cuts of the user set, which determines the minimum sum-rate. For solving this maximization problem and searching for an optimal rate vector, we propose a modified decomposition algorithm (MDA) and a sum-rate increment algorithm (SIA) for asymptotic and non-asymptotic models, respectively, both of which complete in polynomial time. For solving the Dilworth truncation problem as the subroutine in both algorithms, we propose a fusion method to implement the existing coordinate saturation capacity (CoordSatCap) algorithm, where the submodular function minimization (SFM) is done over a merged user set. We show by experimental results that this fusion method contributes to a reduction in computation complexity as compared to the original CoordSatCap algorithm.

Includes members:
  Member "fairness in communication for omniscience":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "info clustering a mathematical theory for data clustering")
  Member "a faster algorithm for asymptotic communication for omniscience":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "part i improving computational efficiency of communication for omniscience")
  Member "info clustering a mathematical theory for data clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secret key agreement for hypergraphical sources with limited total discussion")
  Member "estimating minimum sum rate for cooperative data exchange":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fairness in communication for omniscience")
  Member "iterative merging algorithm for cooperative data exchange":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "part ii a practical approach for successive omniscience")
  Member "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attaining fairness in communication for omniscience")
  Member "structured convex optimization under submodular constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attaining fairness in communication for omniscience")
  Member "coded cooperative data exchange in multihop networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "part i improving computational efficiency of communication for omniscience")

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)