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: "streaming k mismatch with error correcting and applications"
  Abstract: We present a new streaming algorithm for the $k$-Mismatch problem, one of the most basic problems in pattern matching. Given a pattern and a text, the task is to find all substrings of the text that are at the Hamming distance at most $k$ from the pattern. Our algorithm is enhanced with an important new feature called Error Correcting, and its complexities for $k=1$ and for a general $k$ match those of the best known solutions for the $k$-Mismatch problem from FOCS 2009 and SODA 2016. As a corollary we develop a series of streaming algorithms for pattern matching on weighted strings, which are a commonly used representation of uncertain sequences in molecular biology.

Connected through hyperedge HE-1 (induced by source paper "weighted shortest common supersequence problem revisited"):
  with members:
    - "longest common subsequence on weighted sequences"
    - "indexing weighted sequences neat and efficient"
    - "faster space efficient algorithms for subset sum k sum and related problems"
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "efficient index for weighted sequences"

Connected through hyperedge HE-2 (induced by source paper "periodicity in data streams with wildcards"):
  with members:
    - "the streaming k mismatch problem"
    - "streaming periodicity with mismatches"
    - "streaming pattern matching with d wildcards"
    - "tight tradeoffs for real time approximation of longest palindromes in streams"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "space efficient string indexing for wildcard pattern matching"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-3 (induced by source paper "the streaming k mismatch problem"):
  with members:
    - "optimal trade offs for pattern matching with k mismatches"
    - "streaming periodicity with mismatches"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"
    - "space lower bounds for online pattern matching"
    - "parameterized matching in the streaming model"

Connected through hyperedge HE-4 (induced by source paper "pattern matching and consensus problems on weighted sequences and profiles"):
  with members:
    - "efficient index for weighted sequences"
    - "polynomial kernels for weighted problems"
    - "probabilistic threshold indexing for uncertain strings"
    - "fast average case pattern matching on weighted sequences"

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)