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 "a lattice compress and forward scheme"):
  Abstract: We present a nested lattice-code-based strategy that achieves the random-coding based Compress-and-Forward (CF) rate for the three node Gaussian relay channel. To do so, we first outline a lattice-based strategy for the $(X+Z_1,X+Z_2)$ Wyner-Ziv lossy source-coding with side-information problem in Gaussian noise, a re-interpretation of the nested lattice-code-based Gaussian Wyner-Ziv scheme presented by Zamir, Shamai, and Erez. We use the notation $(X+Z_1,X+Z_2)$ Wyner-Ziv to mean that the source is of the form $X+ Z_1$ and the side-information at the receiver is of the form $X+ Z_2$, for independent Gaussian $X, Z_1$ and $Z_2$. We next use this $(X+Z_1,X+Z_2)$ Wyner-Ziv scheme to implement a "structured" or lattice-code-based CF scheme which achieves the classic CF rate for Gaussian relay channels. This suggests that lattice codes may not only be useful in point-to-point single-hop source and channel coding, in multiple access and broadcast channels, but that they may also be useful in larger relay networks. The usage of lattice codes in larger networks is motivated by their structured nature (possibly leading to rate gains) and decoding (relatively simple) being more practically realizable than their random coding based counterparts. We furthermore expect the proposed lattice-based CF scheme to constitute a first step towards a generic structured achievability scheme for networks such as a structured version of the recently introduced "noisy network coding".

Includes members:
  Member "nested lattice codes for gaussian relay networks with interference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized compute compress and forward")
  Member "list decoding for nested lattices and applications to relay channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilevel coding schemes for compute and forward with flexible decoding")
  Member "noisy network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an information theoretic analysis of the gaussian multicast channel with interactive user cooperation")
  Member "lecture notes on network information theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the required number of antennas in a point to point large but finite mimo system outage limited scenario")
  Member "compute and forward harnessing interference through structured codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private information retrieval over gaussian mac")
  Member "a layered lattice coding scheme for a class of three user gaussian interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structured mappings and conferencing common information for multiple access channels")
  Member "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lattice coding for downlink multiuser transmission")

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)