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: "compress forward without wyner ziv binning for the one way and two way relay channels"
  Abstract: We consider the role of Wyner-Ziv binning in compress-forward for relay channels. In the one-way relay channel, we analyze a compress-forward scheme without Wyner- Ziv binning but with joint decoding of both the message and compression index. It achieves the same rate as the original compress-forward scheme with binning and successive decoding. Therefore, binning helps reduce decoding complexity by allowing successive decoding, but has no impact on achievable rate for the one-way relay channel. On the other hand, no binning simplifies relay operation. By extending compress-forward without binning to the two-way relay channel, we can achieve a larger rate region than the original compress-forward scheme when the channel is asymmetric for the two users. Binning and successive decoding limits the compression rate to match the weaker of the channels from relay to two users, whereas without binning, this restriction no longer applies. Compared with noisy network coding, compress-forward without binning achieves the same rate region in certain Gaussian channel configurations, and it has much less delay. This work is a step toward understanding the role of Wyner-Ziv binning in compress-forward relaying.

Connected through hyperedge HE-1 (induced by source paper "on compress forward without wyner ziv binning for relay networks"):
  with members:
    - "incremental relaying for the gaussian interference channel with a degraded broadcasting relay"
    - "short message quantize forward network coding"
    - "lecture notes on network information theory"

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)