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 "the capacity of associated subsequence retrieval"):
  Abstract: The objective of a genome-wide association study (GWAS) is to associate subsequences of individuals' genomes to the observable characteristics called phenotypes (e.g., high blood pressure). Motivated by the GWAS problem, in this paper we introduce the information theoretic problem of \emph{associated subsequence retrieval}, where a dataset of $N$ (possibly high dimensional) sequences of length $G$, and their corresponding observable characteristics is given. The sequences are chosen independently and uniformly at random from $\mathcal{X}^G$, where $\mathcal{X}$ is a finite set. The observable characteristic is assumed to be binary, and it is related to only a specific subsequence of length $L$ of the sequences, called \textit{associated subsequence}. For each sequence, if the associated subsequence of it belongs to some universal finite set, then it is more likely to display the observable characteristic (i.e., it is more likely that the observable characteristic is one). The goal is to retrieve the associated subsequence using a dataset of $N$ sequences and their observable characteristics. We demonstrate that as the parameters $N$, $G$, and $L$ grow, a threshold effect appears in the curve of probability of error versus the rate which is defined as ${Gh(L/G)}/{N}$, where $h(\cdot)$ is the binary entropy function. This effect allows us to define the capacity of associated subsequence retrieval. We develop an achievable scheme and a matching converse for this problem, and thus characterize its capacity in two scenarios: the zero-error-rate and the $\epsilon-$error-rate.

Includes members:
  Member "capacity results for the noisy shuffling channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dna based storage models and fundamental limits")
  Member "fundamental limits of dna storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dna based storage models and fundamental limits")
  Member "rates of dna sequence profiles for practical values of read lengths":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "repeat free codes")
  Member "partial dna assembly a rate distortion perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reconstruction of strings from their substrings spectrum")
  Member "asymmetric lee distance codes for dna based storage":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reconstruction codes for dna sequences with uniform tandem duplication errors")
  Member "do read errors matter for genome assembly":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reconstruction of strings from their substrings spectrum")
  Member "codes for dna sequence profiles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mass error correction codes for polymer based data storage")
  Member "optimal dna shotgun sequencing noisy reads are as good as noiseless reads":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reconstruction of strings from their substrings spectrum")

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)