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: "outlier detection for dna fragment assembly"
  Abstract: A major impediment in the development of ecient full genome sequencing is the large portion of erroneous reads produced by sequencing platforms. Error correction is the computational process that attempts to identify and correct these mistakes. Several classical stringology problems, including the Consensus String problem, are used to model error correction. However, a signicant shortcoming of using these formulations is that they do not account for a few of the reads being too erroneous to correct; these outlier strings potentially have great eect on the solution, and should be detected and removed. We formalize the

Connected through hyperedge HE-1 (induced by source paper "parameterized low rank binary matrix approximation"):
  with members:
    - "approximation schemes for low rank binary matrix approximation problems"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"
    - "generalized independent component analysis over finite alphabets"
    - "fast biclustering by dual parameterization"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-2 (induced by source paper "on approximating string selection problems with outliers"):
  with members:
    - "slightly superexponential parameterized problems"

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.DS(Data Structures and Algorithms)