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: "a faster algorithm for asymptotic communication for omniscience"
  Abstract: We propose a modified decomposition algorithm (MDA) to solve the asymptotic communication for omniscience (CO) problem where the communication rates could be real or fractional. By starting with a lower estimation of the minimum sum-rate, the MDA algorithm iteratively updates the estimation by the optimizer of a Dilworth truncation problem until the minimum is reached with a corresponding optimal rate vector. We also propose a fusion method implementation of the coordinate-wise saturation capacity algorithm (CoordSatCapFus) for solving the Dilworth truncation problem, where the minimization is done over a fused user set with a cardinality smaller than the original one. We show that the MDA algorithm is less complex than the existing ones. In addition, we show that the non-asymptotic CO problem, where the communication rates are integral, can be solved by one more call of the CoordSatCapfus algorithm. By choosing a proper linear ordering of the user indices in the MDA algorithm, the optimal rate vector is also the one with the minimum weighted sum-rate.

Connected through hyperedge HE-1 (induced by source paper "part i improving computational efficiency of communication for omniscience"):
  with members:
    - "part ii a practical approach for successive omniscience"
    - "improving computational efficiency of communication for omniscience and successive omniscience"
    - "secret key agreement under discussion rate constraints"
    - "info clustering a mathematical theory for data clustering"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "efficient algorithms for the data exchange problem"
    - "coded cooperative data exchange in multihop networks"
    - "perfect omniscience perfect secrecy and steiner tree packing"

Connected through hyperedge HE-2 (induced by source paper "part ii a practical approach for successive omniscience"):
  with members:
    - "part i improving computational efficiency of communication for omniscience"
    - "attaining fairness in communication for omniscience"
    - "improving computational efficiency of communication for omniscience and successive omniscience"
    - "successive local and successive global omniscience"
    - "a practical approach for successive omniscience"
    - "universal multiparty data exchange and secret key agreement"
    - "iterative merging algorithm for cooperative data exchange"
    - "efficient algorithms for the data exchange problem"

Connected through hyperedge HE-3 (induced by source paper "attaining fairness in communication for omniscience"):
  with members:
    - "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
    - "structured convex optimization under submodular constraints"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"
    - "on tightness of mutual dependence upperbound for secret key capacity of multiple terminals"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-4 (induced by source paper "improving computational efficiency of communication for omniscience and successive omniscience"):
  with members:
    - "a practical approach for successive omniscience"
    - "iterative merging algorithm for cooperative data exchange"
    - "efficient algorithms for the data exchange problem"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"
    - "on tightness of mutual dependence upperbound for secret key capacity of multiple terminals"

Connected through hyperedge HE-5 (induced by source paper "communication for omniscience"):
  with members:
    - "fairness in communication for omniscience"
    - "info clustering a mathematical theory for data clustering"
    - "estimating minimum sum rate for cooperative data exchange"
    - "iterative merging algorithm for cooperative data exchange"
    - "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
    - "structured convex optimization under submodular constraints"
    - "coded cooperative data exchange in multihop networks"
    - "perfect omniscience perfect secrecy and steiner tree packing"

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)