human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lattice codes for the gaussian relay channel decode and forward and compress and forward"):
  Abstract: Lattice codes are known to achieve capacity in the Gaussian point-to-point channel, achieving the same rates as independent, identically distributed (i.i.d.) random Gaussian codebooks. Lattice codes are also known to outperform random codes for certain channel models that are able to exploit their linearity. In this work, we show that lattice codes may be used to achieve the same performance as known i.i.d. Gaussian random coding techniques for the Gaussian relay channel, and show several examples of how this may be combined with the linearity of lattices codes in multi-source relay networks. In particular, we present a nested lattice list decoding technique, by which, lattice codes are shown to achieve the Decode-and-Forward (DF) rate of single source, single destination Gaussian relay channels with one or more relays. We next present two examples of how this DF scheme may be combined with the linearity of lattice codes to achieve new rate regions which for some channel conditions outperform analogous known Gaussian random coding techniques in multi-source relay channels. That is, we derive a new achievable rate region for the two-way relay channel with direct links and compare it to existing schemes, and derive another achievable rate region for the multiple access relay channel. We furthermore present a lattice Compress-and-Forward (CF) scheme for the Gaussian relay channel which exploits a lattice Wyner-Ziv binning scheme and achieves the same rate as the Cover-El Gamal CF rate evaluated for Gaussian random codes. These results suggest that structured/lattice codes may be used to mimic, and sometimes outperform, random Gaussian codes in general Gaussian networks.

Includes members:
  Member "the approximate sum capacity of the symmetric gaussian k user interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private information retrieval over gaussian mac")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "reliable physical layer network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards massive connectivity support for scalable mmtc communications in 5g networks")
  Member "nested lattice codes for gaussian relay networks with interference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized compute compress and forward")
  Member "computation alignment capacity approximation without noise accumulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations")
  Member "structured interference mitigation in two hop networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relay channel with orthogonal components and structured interference known at the source")
  Member "a lattice compress and forward scheme":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "achievable rate regions for two way relay channel using nested lattice coding")
  Member "interference alignment at finite snr for time invariant channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "feasibility of interference alignment for the mimo interference channel")

Question: Please tell me which class the source paper 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)