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: "density evolution for asymmetric memoryless channels"
  Abstract: Density evolution (DE) is one of the most powerful analytical tools for low-density parity-check (LDPC) codes and graph codes with message passing decoding algorithms. With channel symmetry as one of its fundamental assumptions, density evolution has been widely and successfully applied to different channels, including binary erasure channels (BECs), binary symmetric channels (BSCs), binary additive white Gaussian noise (BiAWGN) channels, etc. This paper generalizes density evolution for asymmetric memoryless channels, which in turn broadens the applications to general memoryless channels, e.g., z-channels, composite white Gaussian noise channels, etc. The central theorem underpinning this generalization is the convergence to perfect projection for any fixed-size supporting tree. A new iterative formula of the same complexity is then presented and the necessary theorems for the performance concentration theorems are developed. Several properties of the new density evolution method are explored, including stability results for general asymmetric memoryless channels. Simulations, code optimizations, and possible new applications suggested by this new density evolution method are also provided. This result is also used to prove the typicality of linear LDPC codes among the coset code ensemble when the minimum check node degree is sufficiently large. It is shown that the convergence to perfect projection is essential to the belief propagation (BP) algorithm even when only symmetric channels are considered. Hence, the proof of the convergence to perfect projection serves also as a completion of the theory of classical density evolution for symmetric memoryless channels.

Connected through hyperedge HE-1 (induced by source paper "asymptotic analysis on ldpc bicm scheme for compute and forward relaying"):
  with members:
    - "asymptotic analysis of spatial coupling coding for compute and forward relaying"
    - "secure computation and forward with linear codes"
    - "an algebraic approach to physical layer network coding"
    - "universal codes for the gaussian mac via spatial coupling"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "can iterative decoding for erasure correlated sources be universal"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-2 (induced by source paper "asymptotic analysis on spatial coupling coding for two way relay channels"):
  with members:
    - "physical layer network coding a random coding error exponent perspective"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "compute and forward harnessing interference through structured codes"
    - "joint physical layer coding and network coding for bi directional relaying"

Connected through hyperedge HE-3 (induced by source paper "analysis and design of finite alphabet iterative decoders robust to faulty hardware"):
  with members:
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "performance of ldpc codes under faulty iterative decoding"
    - "design and analysis of nonbinary ldpc codes for arbitrary discrete memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "how to achieve the capacity of asymmetric channels"):
  with members:
    - "on the scaling exponent of polar codes for binary input energy harvesting channels"
    - "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel"
    - "construction of polar codes with sublinear complexity"
    - "polar codes for broadcast channels"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "achieving marton s region for broadcast channels using polar codes"

Connected through hyperedge HE-5 (induced by source paper "statistical mechanical analysis of low density parity check codes on general markov channel"):
  with members:
    - "connection between annealed free energy and belief propagation on random factor graph ensembles"
    - "the generalized area theorem and some of its consequences"
    - "tight bounds for ldpc and ldgm codes under map decoding"

Connected through hyperedge HE-6 (induced by source paper "joint network and ldpc coding for bi directional relaying"):
  with members:
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "physical network coding in two way wireless relay channels"

Connected through hyperedge HE-7 (induced by source paper "quantization of binary input discrete memoryless channels"):
  with members:
    - "at low snr asymmetric quantizers are better"
    - "how to construct polar codes"

Connected through hyperedge HE-8 (induced by source paper "modern coding theory the statistical mechanics and computer science point of view"):
  with members:
    - "on the asymptotic weight and stopping set distribution of regular ldpc ensembles"
    - "counting good truth assignments of random k sat formulae"
    - "the generalized area theorem and some of its consequences"
    - "tight bounds for ldpc and ldgm codes under map decoding"
    - "finite length scaling for iteratively decoded ldpc ensembles"
    - "a new look at survey propagation and its generalizations"

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)