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: "partial dna assembly a rate distortion perspective"
  Abstract: Earlier formulations of the DNA assembly problem were all in the context of perfect assembly; i.e., given a set of reads from a long genome sequence, is it possible to perfectly reconstruct the original sequence? In practice, however, it is very often the case that the read data is not sufficiently rich to permit unambiguous reconstruction of the original sequence. While a natural generalization of the perfect assembly formulation to these cases would be to consider a rate-distortion framework, partial assemblies are usually represented in terms of an assembly graph, making the definition of a distortion measure challenging. In this work, we introduce a distortion function for assembly graphs that can be understood as the logarithm of the number of Eulerian cycles in the assembly graph, each of which correspond to a candidate assembly that could have generated the observed reads. We also introduce an algorithm for the construction of an assembly graph and analyze its performance on real genomes.

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"
    - "asymmetric lee distance codes for dna based storage"
    - "do read errors matter for genome assembly"
    - "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"
    - "do read errors matter for genome assembly"
    - "string reconstruction from substring compositions"
    - "optimal dna shotgun sequencing noisy reads are as good as noiseless reads"
    - "information theory of dna shotgun sequencing"

Connected through hyperedge HE-3 (induced by source paper "models and information theoretic bounds for nanopore sequencing"):
  with members:
    - "capacity upper bounds for deletion type channels"

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)