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: "joint signal and channel state information compression for the backhaul of uplink network mimo systems"
  Abstract: In network MIMO cellular systems, subsets of base stations (BSs), or remote radio heads, are connected via backhaul links to central units (CUs) that perform joint encoding in the downlink and joint decoding in the uplink. Focusing on the uplink, an effective solution for the communication between BSs and the corresponding CU on the backhaul links is based on compressing and forwarding the baseband received signal from each BS. In the presence of ergodic fading, communicating the channel state information (CSI) from the BSs to the CU may require a sizable part of the backhaul capacity. In a prior work, this aspect was studied by assuming a Compress-Forward-Estimate (CFE) approach, whereby the BSs compress the training signal and CSI estimation takes place at the CU. In this work, instead, an Estimate-Compress-Forward (ECF) approach is investigated, whereby the BSs perform CSI estimation and forward a compressed version of the CSI to the CU. This choice is motivated by the information theoretic optimality of separate estimation and compression. Various ECF strategies are proposed that perform either separate or joint compression of estimated CSI and received signal. Moreover, the proposed strategies are combined with distributed source coding when considering multiple BSs. "Semi-coherent" strategies are also proposed that do not convey any CSI or training information on the backhaul links. Via numerical results, it is shown that a proper design of ECF strategies based on joint received signal and estimated CSI compression or of semi-coherent schemes leads to substantial performance gains compared to more conventional approaches based on non-coherent transmission or the CFE approach.

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)