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: "optimal haplotype assembly from high throughput mate pair reads"
  Abstract: Humans have $23$ pairs of homologous chromosomes. The homologous pairs are almost identical pairs of chromosomes. For the most part, differences in homologous chromosome occur at certain documented positions called single nucleotide polymorphisms (SNPs). A haplotype of an individual is the pair of sequences of SNPs on the two homologous chromosomes. In this paper, we study the problem of inferring haplotypes of individuals from mate-pair reads of their genome. We give a simple formula for the coverage needed for haplotype assembly, under a generative model. The analysis here leverages connections of this problem with decoding convolutional codes.

Connected through hyperedge HE-1 (induced by source paper "multi observation regression"):
  with members:
    - "multi observation elicitation"
    - "learning deep embeddings with histogram loss"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "community recovery in graphs with locality"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "near optimal joint object matching via convex relaxation"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-3 (induced by source paper "fundamental limits of pooled dna sequencing"):
  with members:
    - "information theory of dna shotgun sequencing"

Connected through hyperedge HE-4 (induced by source paper "information recovery from pairwise measurements"):
  with members:
    - "community recovery in graphs with locality"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "bounds among f divergences"
    - "discrete signal processing on graphs sampling theory"
    - "near optimal joint object matching via convex relaxation"
    - "accurate community detection in the stochastic block model via spectral algorithms"

Connected through hyperedge HE-5 (induced by source paper "information recovery from pairwise measurements a shannon theoretic approach"):
  with members:
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "near optimal joint object matching via convex relaxation"
    - "minimax estimation of functionals of discrete distributions"
    - "tight error bounds for structured prediction"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "renyi divergence and kullback leibler divergence"
    - "exact recovery in the stochastic block model"
    - "haplotype assembly an information theoretic view"

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)