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: "successive local and successive global omniscience"
  Abstract: This paper considers two generalizations of the cooperative data exchange problem, referred to as the successive local omniscience (SLO) and the successive global omniscience (SGO). The users are divided into $\ell$ nested sub-groups. Each user initially knows a subset of packets in a ground set $X$ of size $k$, and all users wish to learn all packets in $X$. The users exchange their packets by broadcasting coded or uncoded packets. In SLO or SGO, respectively, the $l$th ($1\leq l\leq \ell$) smallest sub-group of users need to learn all packets they collectively hold or all packets in $X$, in the $l$th round of transmissions. The problem is to find the minimum sum-rate (i.e., the total transmission rate by all users) for each round, subject to minimizing the sum-rate for the previous round. To solve this problem, we use a linear-programming approach. For the cases in which the packets are randomly distributed among users, we construct a system of linear equations whose solution characterizes the minimum sum-rate for each round with high probability as $k$ tends to infinity. Moreover, for the special case of two nested groups, we derive closed-form expressions, which hold with high probability as $k$ tends to infinity, for the minimum sum-rate for each round.

Connected through hyperedge HE-1 (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"
    - "a practical approach for successive omniscience"
    - "universal multiparty data exchange and secret key agreement"
    - "a faster algorithm for asymptotic communication for omniscience"
    - "iterative merging algorithm for cooperative data exchange"
    - "efficient algorithms for the data exchange problem"

Connected through hyperedge HE-2 (induced by source paper "cooperative data exchange based on mds codes"):
  with members:
    - "universal multiparty data exchange and secret key agreement"
    - "cooperative data exchange with unreliable clients"
    - "efficient algorithms for the data exchange problem"
    - "balanced sparsest generator matrices for mds codes"
    - "coded cooperative data exchange in multihop networks"
    - "coded cooperative data exchange problem for general topologies"
    - "on coding for cooperative data exchange"

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)