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: "do read errors matter for genome assembly"
  Abstract: While most current high-throughput DNA sequencing technologies generate short reads with low error rates, emerging sequencing technologies generate long reads with high error rates. A basic question of interest is the tradeoff between read length and error rate in terms of the information needed for the perfect assembly of the genome. Using an adversarial erasure error model, we make progress on this problem by establishing a critical read length, as a function of the genome and the error rate, above which perfect assembly is guaranteed. For several real genomes, including those from the GAGE dataset, we verify that this critical read length is not significantly greater than the read length required for perfect assembly from reads without errors.

Connected through hyperedge HE-1 (induced by source paper "the capacity of associated subsequence retrieval"):
  with members:
    - "capacity results for the noisy shuffling channel"
    - "fundamental limits of dna storage systems"
    - "rates of dna sequence profiles for practical values of read lengths"
    - "partial dna assembly a rate distortion perspective"
    - "asymmetric lee distance codes for dna based storage"
    - "codes for dna sequence profiles"
    - "optimal dna shotgun sequencing noisy reads are as good as noiseless reads"
    - "information theory of dna shotgun sequencing"

Connected through hyperedge HE-2 (induced by source paper "reconstruction of strings from their substrings spectrum"):
  with members:
    - "repeat free codes"
    - "unique reconstruction of coded strings from multiset substring spectra"
    - "mutually uncorrelated codes for dna storage"
    - "partial dna assembly a rate distortion perspective"
    - "string reconstruction from substring compositions"
    - "optimal dna shotgun sequencing noisy reads are as good as noiseless reads"
    - "information theory of dna shotgun sequencing"

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)