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: "communicating correlated sources over mac and interference channels i separation based schemes"
  Abstract: We consider the two scenarios of communicating a pair $S_{1},S_{2}$ of correlated sources over multiple access (MAC) and interference channels (IC) respectively. We undertake a Shannon theoretic study and focus on achievability, i.e., characterizing sufficient conditions. In the absence of a Ga\'cs-K\"orner-Witsenhausen common part, the current known single-letter (S-L) coding schemes are constrained to the S-L long Markov Chain (LMC) $X_{1}-S_{1}-S_{2}-X_{2}$. Taking the lead of Dueck's example [Dueck, Mar 1981], we recognize that the latter constraint is debilitating, leading to sub-optimality of S-L coding schemes. The goal of our work is to design a coding scheme wherein (i) the choice of channel input at time $t$ is based on multiple source symbols, and is yet ii) amenable to performance characterization via S-L expressions. In this article, we present the first part of our findings. We propose a new separation-based coding scheme based on a fixed block-length (B-L) codes that enables choice of $X_{jt}$ - the symbol input on the channel by encoder $j$ at time $t$ - to be based on a generic number $l$ of source symbols $S_{j}^{l}$, thus permitting correlation of the input symbols $X_{1},X_{2}$ through a multi-letter LMC $X_{1}-S_{1}^{l}-S_{2}^{l}-X_{2}$. By carefully stitching together S-L coding techniques we devise a multi-letter coding scheme. We characterize an inner bound to its performance via a S-L expression and prove that the derived inner bound is strictly larger than the current known largest inner bounds for both the MAC and IC problems based on S-L coding schemes. In the second part of our work, we propose to enlarge the inner bound derived in this article by incorporating the technique of inducing source correlation onto channel inputs [Cover, El Gamal and Salehi, Nov 1980].

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)