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: "on the tanner graph cycle distribution of random ldpc random protograph based ldpc and random quasi cyclic ldpc code ensembles"
  Abstract: In this paper, we study the cycle distribution of random low-density parity-check (LDPC) codes, randomly constructed protograph-based LDPC codes, and random quasi-cyclic (QC) LDPC codes. We prove that for a random bipartite graph, with a given (irregular) degree distribution, the distributions of cycles of different length tend to independent Poisson distributions, as the size of the graph tends to infinity. We derive asymptotic upper and lower bounds on the expected values of the Poisson distributions that are independent of the size of the graph, and only depend on the degree distribution and the cycle length. For a random lift of a bi-regular protograph, we prove that the asymptotic cycle distributions are essentially the same as those of random bipartite graphs as long as the degree distributions are identical. For random QC-LDPC codes, however, we show that the cycle distribution can be quite different from the other two categories. In particular, depending on the protograph and the value of $c$, the expected number of cycles of length $c$, in this case, can be either $\Theta(N)$ or $\Theta(1)$, where $N$ is the lifting degree (code length). We also provide numerical results that match our theoretical derivations. Our results provide a theoretical foundation for emperical results that were reported in the literature but were not well-justified. They can also be used for the analysis and design of LDPC codes and associated algorithms that are based on cycles.

Connected through hyperedge HE-1 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention"):
  with members:
    - "from learning to meta learning reduced training overhead and complexity for communication systems"
    - "data driven ensembles for deep and hard decision hybrid decoding"
    - "active deep decoding of linear codes"
    - "data efficient mutual information neural estimator"
    - "a new permutation decoding method for reed muller codes"
    - "unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "cospectral bipartite graphs with the same degree sequences but with different number of large cycles"):
  with members:
    - "on computing the multiplicity of cycles in bipartite graphs using the degree distribution and the spectrum of the graph"
    - "on short cycle enumeration in biregular bipartite graphs"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "lowering the error floor of ldpc codes using cyclic liftings"

Connected through hyperedge HE-3 (induced by source paper "outage limit approaching protograph ldpc codes for slow fading wireless communications"):
  with members:
    - "improving the decoding threshold of tailbiting spatially coupled ldpc codes by energy shaping"
    - "construction of near capacity protograph ldpc code sequences with block error thresholds"
    - "quasi cyclic ldpc codes based on pre lifted protographs"
    - "low density graph codes for coded cooperation on slow fading relay channels"

Connected through hyperedge HE-4 (induced by source paper "on computing the number of short cycles in bipartite graphs using the spectrum of the directed edge matrix"):
  with members:
    - "on short cycle enumeration in biregular bipartite graphs"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"

Connected through hyperedge HE-5 (induced by source paper "characterization and efficient search of non elementary trapping sets of ldpc codes with applications to stopping sets"):
  with members:
    - "characterization and efficient exhaustive search algorithm for elementary trapping sets of irregular ldpc codes"
    - "on the minimum stopping distance of array low density parity check codes"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "bounds on the minimum distance of punctured quasi cyclic ldpc codes"

Connected through hyperedge HE-6 (induced by source paper "on computing the multiplicity of cycles in bipartite graphs using the degree distribution and the spectrum of the graph"):
  with members:
    - "on short cycle enumeration in biregular bipartite graphs"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "lowering the error floor of ldpc codes using cyclic liftings"

Connected through hyperedge HE-7 (induced by source paper "counting short cycles of c d regular bipartite graphs"):
  with members:
    - "on computing the multiplicity of cycles in bipartite graphs using the degree distribution and the spectrum of the graph"
    - "on short cycle enumeration in biregular bipartite graphs"

Connected through hyperedge HE-8 (induced by source paper "hardness results on finding leafless elementary trapping sets and elementary absorbing sets of ldpc codes"):
  with members:
    - "characterization and efficient search of non elementary trapping sets of ldpc codes with applications to stopping sets"
    - "asymptotic average number of different categories of trapping sets absorbing sets and stopping sets in random regular and irregular ldpc code ensembles"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "lowering the error floor of ldpc codes using cyclic liftings"
    - "a general method for finding low error rates of ldpc codes"

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)