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 compress forward without wyner ziv binning for relay networks"
  Abstract: Noisy network coding is recently proposed for the general multi-source network by Lim, Kim, El Gamal and Chung. This scheme builds on compress-forward (CF) relaying but involves three new ideas, namely no Wyner-Ziv binning, relaxed simultaneous decoding and message repetition. In this paper, using the two-way relay channel as the underlining example, we analyze the impact of each of these ideas on the achievable rate region of relay networks. First, CF without binning but with joint decoding of both the message and compression index can achieve a larger rate region than the original CF scheme for multi-destination relay networks. With binning and successive decoding, the compression rate at each relay is constrained by the weakest link from the relay to a destination; but without binning, this constraint is relaxed. Second, simultaneous decoding of all messages over all blocks without uniquely decoding the compression indices can remove the constraints on compression rate completely, but is still subject to the message block boundary effect. Third, message repetition is necessary to overcome this boundary effect and achieve the noisy network coding region for multi-source networks. The rate region is enlarged with increasing repetition times. We also apply CF without binning specifically to the one-way and two-way relay channels and analyze the rate regions in detail. For the one-way relay channel, it achieves the same rate as the original CF and noisy network coding but has only 1 block decoding delay. For the two-way relay channel, we derive the explicit channel conditions in the Gaussian and fading cases for CF without binning to achieve the same rate region or sum rate as noisy network coding. These analyses may be appealing to practical implementation because of the shorter encoding and decoding delay in CF without binning.

Connected through hyperedge HE-1 (induced by source paper "short message noisy network coding with a decode forward option"):
  with members:
    - "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows"
    - "mixed noisy network coding and cooperative unicasting in wireless networks"
    - "a unified relay framework with both d f and c f relay nodes"
    - "wireless network information flow a deterministic approach"
    - "short message quantize forward network coding"
    - "slepian wolf coding over cooperative relay networks"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "noisy network coding"

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)