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 dna shotgun sequencing noisy reads are as good as noiseless reads"
  Abstract: We establish the fundamental limits of DNA shotgun sequencing under noisy reads. We show a surprising result: for the i.i.d. DNA model, noisy reads are as good as noiseless reads, provided that the noise level is below a certain threshold which can be surprisingly high. As an example, for a uniformly distributed DNA sequence and a symmetric substitution noisy read channel, the threshold is as high as 19%.

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"
    - "do read errors matter for genome assembly"
    - "codes for dna sequence profiles"
    - "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"
    - "do read errors matter for genome assembly"
    - "string reconstruction from substring compositions"
    - "information theory of dna shotgun sequencing"

Connected through hyperedge HE-3 (induced by source paper "unaligned sequence similarity search using deep learning"):
  with members:
    - "finding your lookalike measuring face similarity rather than face identity"
    - "facenet a unified embedding for face recognition and clustering"
    - "efficient estimation of word representations in vector space"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "do read errors matter for genome assembly"):
  with members:
    - "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)