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: "distributed compression for the uplink of a backhaul constrained coordinated cellular network"
  Abstract: We consider a backhaul-constrained coordinated cellular network. That is, a single-frequency network with $N+1$ multi-antenna base stations (BSs) that cooperate in order to decode the users' data, and that are linked by means of a common lossless backhaul, of limited capacity $\mathrm{R}$. To implement receive cooperation, we propose distributed compression: $N$ BSs, upon receiving their signals, compress them using a multi-source lossy compression code. Then, they send the compressed vectors to a central BS, which performs users' decoding. Distributed Wyner-Ziv coding is proposed to be used, and is optimally designed in this work. The first part of the paper is devoted to a network with a unique multi-antenna user, that transmits a predefined Gaussian space-time codeword. For such a scenario, the compression codebooks at the BSs are optimized, considering the user's achievable rate as the performance metric. In particular, for $N = 1$ the optimum codebook distribution is derived in closed form, while for $N>1$ an iterative algorithm is devised. The second part of the contribution focusses on the multi-user scenario. For it, the achievable rate region is obtained by means of the optimum compression codebooks for sum-rate and weighted sum-rate, respectively.

Connected through hyperedge HE-1 (induced by source paper "cooperative channel estimation for coordinated transmission with limited backhaul"):
  with members:
    - "csit sharing over finite capacity backhaul for spatial interference alignment"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "robust monotonic optimization framework for multicell miso systems"
    - "uplink comp under a constrained backhaul and imperfect channel knowledge"
    - "k means has polynomial smoothed complexity"
    - "adaptive limited feedback for sum rate maximizing beamforming in cooperative multicell systems"

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)