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 "improving computational efficiency of communication for omniscience and successive omniscience"):
  Abstract: For a group of users in $V$ where everyone observes a component of a discrete multiple random source, the process that users exchange data so as to reach omniscience, the state where everyone recovers the entire source, is called communication for omniscience (CO). We first consider how to improve the existing complexity $O(|V|^2 \cdot \text{SFM}(|V|)$ of minimizing the sum of communication rates in CO, where $\text{SFM}(|V|)$ denotes the complexity of minimizing a submodular function. We reveal some structured property in an existing coordinate saturation algorithm: the resulting rate vector and the corresponding partition of $V$ are segmented in $\alpha$, the estimation of the minimum sum-rate. A parametric (PAR) algorithm is then proposed where, instead of a particular $\alpha$, we search the critical points that fully determine the segmented variables for all $\alpha$ so that they converge to the solution to the minimum sum-rate problem and the overall complexity reduces to $O(|V| \cdot \text{SFM}(|V|))$. #R##N#For the successive omniscience (SO), we consider how to attain local omniscience in some complimentary user subset so that the overall sum-rate for the global omniscience still remains minimum. While the existing algorithm only determines a complimentary user subset in $O(|V| \cdot \text{SFM}(|V|))$ time, we show that, if a lower bound on the minimum sum-rate is applied to the segmented variables in the PAR algorithm, not only a complimentary subset, but also an optimal rate vector for attaining the local omniscience in it are returned in $O(|V| \cdot \text{SFM}(|V|))$ time.

Includes members:
  Member "a practical approach for successive omniscience":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "part ii a practical approach for successive omniscience")
  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 "iterative merging algorithm for cooperative data exchange":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "part ii a practical approach for successive omniscience")
  Member "efficient algorithms for the data exchange problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "part i improving computational efficiency of communication for omniscience")
  Member "coded cooperative data exchange in multihop networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "part i improving computational efficiency of communication for omniscience")
  Member "on coding for cooperative data exchange":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "embedded index coding")
  Member "on tightness of mutual dependence upperbound for secret key capacity of multiple terminals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication for generating correlation a unifying survey")

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)