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: "the case for structured random codes in network capacity theorems"
  Abstract: Random coding arguments are the backbone of most channel capacity achievability proofs. In this paper, we show that in their standard form, such arguments are insufficient for proving some network capacity theorems: structured coding arguments, such as random linear or lattice codes, attain higher rates. Historically, structured codes have been studied as a stepping stone to practical constructions. However, K\"{o}rner and Marton demonstrated their usefulness for capacity theorems through the derivation of the optimal rate region of a distributed functional source coding problem. Here, we use multicasting over finite field and Gaussian multiple-access networks as canonical examples to demonstrate that even if we want to send bits over a network, structured codes succeed where simple random codes fail. Beyond network coding, we also consider distributed computation over noisy channels and a special relay-type problem.

Connected through hyperedge HE-1 (induced by source paper "lattice codes achieve the capacity of common message gaussian broadcast channels with coded side information"):
  with members:
    - "capacity of coded index modulation"
    - "index codes for the gaussian broadcast channel using quadrature amplitude modulation"
    - "a lattice approach for optimal rate diverse wireless network coding"
    - "lattice index coding"
    - "index coding with coded side information"
    - "lattice index codes from algebraic number fields"
    - "joint network and gelfand pinsker coding for 3 receiver gaussian broadcast channels with receiver message side information"
    - "the capacity of three receiver awgn broadcast channels with receiver message side information"

Connected through hyperedge HE-2 (induced by source paper "analytical framework of ldgm based iterative quantization with decimation"):
  with members:
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "average entropy functions"
    - "design and analysis of ldgm based codes for mse quantization"
    - "low density graph codes that are optimal for source channel coding and binning"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "analysis of ldgm and compound codes for lossy compression and binning"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"

Connected through hyperedge HE-3 (induced by source paper "multiple input multiple output two way relaying a space division approach"):
  with members:
    - "eigen direction alignment based physical layer network coding for mimo two way relay channels"
    - "physical layer mimo relaying"
    - "compute and forward harnessing interference through structured codes"
    - "optimal beamforming for two way multi antenna relay channel with analogue network coding"
    - "physical network coding in two way wireless relay channels"
    - "capacity of a class of modulo sum relay channels"

Connected through hyperedge HE-4 (induced by source paper "on the equal rate capacity of the awgn multiway relay channel"):
  with members:
    - "the capacity region of multiway relay channels over finite fields with full data exchange"
    - "an optimal coding strategy for the binary multi way relay channel"
    - "the multi way relay channel"
    - "compute and forward harnessing interference through structured codes"
    - "physical layer network coding"

Connected through hyperedge HE-5 (induced by source paper "reliable physical layer network coding"):
  with members:
    - "an algebraic approach to physical layer network coding"
    - "divide and conquer approaching the capacity of the two pair bidirectional gaussian relay network"
    - "wireless network information flow a deterministic approach"
    - "nested lattice codes for gaussian relay networks with interference"
    - "on achieving local view capacity via maximal independent graph scheduling"
    - "list decoding for nested lattices and applications to relay channels"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "capacity theorems for the awgn multi way relay channel"

Connected through hyperedge HE-6 (induced by source paper "the capacity region of multiway relay channels over finite fields with full data exchange"):
  with members:
    - "nested lattice codes for gaussian relay networks with interference"
    - "an optimal coding strategy for the binary multi way relay channel"
    - "the multi way relay channel"
    - "capacity theorems for the awgn multi way relay channel"
    - "lossy broadcasting in two way relay networks with common reconstructions"
    - "capacity region of the deterministic multi pair bi directional relay network"
    - "broadcast capacity region of two phase bidirectional relaying"
    - "approximate capacity of the two way relay channel a deterministic approach"

Connected through hyperedge HE-7 (induced by source paper "interference decoding for deterministic channels"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "lecture notes on network information theory"
    - "on the degrees of freedom of the k user gaussian interference channel"
    - "on the sum capacity of a class of cyclically symmetric deterministic interference channels"
    - "capacity of a class of symmetric simo gaussian interference channels within o 1"
    - "compute and forward harnessing interference through structured codes"
    - "the two user gaussian interference channel a deterministic view"

Connected through hyperedge HE-8 (induced by source paper "nested lattice codes for gaussian relay networks with interference"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "lattice strategies for the dirty multiple access channel"
    - "a layered lattice coding scheme for a class of three user gaussian interference channels"
    - "joint physical layer coding and network coding for bi directional relaying"
    - "approximate capacity of gaussian relay networks"
    - "wireless network information flow"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

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)