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: "on approximating the sum rate for multiple unicasts"
  Abstract: We study upper bounds on the sum-rate of multiple-unicasts. We approximate the Generalized Network Sharing Bound (GNS cut) of the multiple-unicasts network coding problem with $k$ independent sources. Our approximation algorithm runs in polynomial time and yields an upper bound on the joint source entropy rate, which is within an $O(\log^2 k)$ factor from the GNS cut. It further yields a vector-linear network code that achieves joint source entropy rate within an $O(\log^2 k)$ factor from the GNS cut, but \emph{not} with independent sources: the code induces a correlation pattern among the sources. #R##N#Our second contribution is establishing a separation result for vector-linear network codes: for any given field $\mathbb{F}$ there exist networks for which the optimum sum-rate supported by vector-linear codes over $\mathbb{F}$ for independent sources can be multiplicatively separated by a factor of $k^{1-\delta}$, for any constant ${\delta>0}$, from the optimum joint entropy rate supported by a code that allows correlation between sources. Finally, we establish a similar separation result for the asymmetric optimum vector-linear sum-rates achieved over two distinct fields $\mathbb{F}_{p}$ and $\mathbb{F}_{q}$ for independent sources, revealing that the choice of field can heavily impact the performance of a linear network code.

Connected through hyperedge HE-1 (induced by source paper "a bound on the shannon capacity via a linear programming variation"):
  with members:
    - "local graph coloring and index coding"
    - "broadcasting with side information"

Connected through hyperedge HE-2 (induced by source paper "structural properties of index coding capacity"):
  with members:
    - "fractional coloring orthogonal access achieves all unicast capacity dof region of index coding tim if and only if network topology is chordal"
    - "three stories on a two sided coin index coding locally recoverable distributed storage and guessing games on graphs"
    - "on a duality between recoverable distributed storage and index coding"
    - "a rate distortion approach to index coding"
    - "topological interference management through index coding"
    - "fundamental limits of caching in wireless d2d networks"
    - "bounding multiple unicasts through index coding and locally repairable codes"
    - "critical graphs in index coding"

Connected through hyperedge HE-3 (induced by source paper "generalized lexicographic products and the index coding capacity"):
  with members:
    - "approximate capacity of index coding for some classes of graphs"
    - "tdma is optimal for all unicast dof region of tim if and only if topology is chordal bipartite"
    - "three stories on a two sided coin index coding locally recoverable distributed storage and guessing games on graphs"
    - "structural properties of index coding capacity using fractional graph theory"
    - "on a duality between recoverable distributed storage and index coding"
    - "topological interference management through index coding"
    - "fundamental limits of caching in wireless d2d networks"
    - "bounding multiple unicasts through index coding and locally repairable codes"

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)