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: "ldpc code design for transmission of correlated sources across noisy channels without csit"
  Abstract: We consider the problem of transmitting correlated data after independent encoding to a central receiver through orthogonal channels. We assume that the channel state information is not known at the transmitter. The receiver has access to both the source correlation and the channel state information. We provide a generic framework for analyzing the performance of joint iterative decoding, using density evolution. Using differential evolution, we design punctured systematic LDPC codes to maximize the region of achievable channel conditions, with joint iterative decoding. The main contribution of this paper is to demonstrate that properly designed LDPC can perform well simultaneously over a wide range of channel parameters.

Connected through hyperedge HE-1 (induced by source paper "spatially coupled ldpc codes for decode and forward relaying of two correlated sources over the bec"):
  with members:
    - "source channel coding theorems for the multiple access relay channel"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "universality for the noisy slepian wolf problem via spatial coupling"
    - "bilayer ldpc convolutional codes for half duplex relay channels"
    - "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"
    - "can iterative decoding for erasure correlated sources be universal"
    - "bilayer low density parity check codes for decode and forward in relay channels"
    - "network information flow with correlated sources"

Connected through hyperedge HE-2 (induced by source paper "code design for the noisy slepian wolf problem"):
  with members:
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "universality for the noisy slepian wolf problem via spatial coupling"
    - "universal codes for the gaussian mac via spatial coupling"
    - "can iterative decoding for erasure correlated sources be universal"
    - "slepian wolf code design via source channel correspondence"
    - "network information flow with correlated sources"

Connected through hyperedge HE-3 (induced by source paper "universal codes for the gaussian mac via spatial coupling"):
  with members:
    - "threshold saturation on channels with memory via spatial coupling"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "universality for the noisy slepian wolf problem via spatial coupling"
    - "coupled graphical models and their thresholds"
    - "spatially coupled codes over the multiple access channel"
    - "the effect of spatial coupling on compressive sensing"
    - "threshold saturation on bms channels via spatial coupling"
    - "rate equivocation optimal spatially coupled ldpc codes for the bec wiretap channel"

Connected through hyperedge HE-4 (induced by source paper "universality for the noisy slepian wolf problem via spatial coupling"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "threshold saturation on bms channels via spatial coupling"
    - "can iterative decoding for erasure correlated sources be universal"
    - "slepian wolf code design via source channel correspondence"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the generalized area theorem and some of its consequences"
    - "network information flow with correlated sources"

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)