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: "the streaming k mismatch problem"
  Abstract: We consider the problem of approximate pattern matching in a stream. In the streaming $k$-mismatch problem, we must compute all Hamming distances between a pattern of length $n$ and successive $n$-length substrings of a longer text, as long as the Hamming distance is at most $k$. The twin challenges of streaming pattern matching derive from the need both to achieve small and typically sublinear working space and also to have fast guaranteed running time for every arriving symbol of the text. #R##N#As a preliminary step we first give a deterministic $O(k(\log \frac{n}{k} + \log |\Sigma|) )$-bit encoding of all the alignments with Hamming distance at most $k$ between a pattern and a text of length $O(n)$. (We denote the input alphabet by the symbol $\Sigma$.) This result, which provides a solution for a seemingly difficult combinatorial problem, may be of independent interest. We then go on to give an $O(k\log^3 n\log\frac{n}{k})$-time streaming algorithm for the $k$-mismatch streaming problem which uses only $O(k\log{n}\log\frac{n}{k})$ bits of space. The space usage is within logarithmic factors of optimal and approximately a factor of $k$ improvement over the previous record [Clifford et al., SODA 2016]

Connected through hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances"):
  with members:
    - "l p pattern matching in a stream"
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"

Connected through hyperedge HE-2 (induced by source paper "approximating text to pattern distance via dimensionality reduction"):
  with members:
    - "l p pattern matching in a stream"
    - "approximating approximate pattern matching"
    - "approximate hamming distance in a stream"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-3 (induced by source paper "l p pattern matching in a stream"):
  with members:
    - "approximating approximate pattern matching"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"
    - "how to catch l 2 heavy hitters on sliding windows"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-4 (induced by source paper "circular pattern matching with k mismatches"):
  with members:
    - "efficient representation and counting of antipower factors in words"
    - "the k mismatch problem revisited"
    - "average case optimal approximate circular string matching"
    - "internal pattern matching queries in a text and applications"
    - "beating o nm in approximate lzw compressed pattern matching"

Connected through hyperedge HE-5 (induced by source paper "periodicity in data streams with wildcards"):
  with members:
    - "streaming periodicity with mismatches"
    - "streaming pattern matching with d wildcards"
    - "tight tradeoffs for real time approximation of longest palindromes in streams"
    - "streaming k mismatch with error correcting and applications"
    - "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-6 (induced by source paper "streaming pattern matching with d wildcards"):
  with members:
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "space lower bounds for online pattern matching"
    - "parameterized matching in the streaming model"
    - "fast moment estimation in data streams in optimal space"

Connected through hyperedge HE-7 (induced by source paper "streaming k mismatch with error correcting and applications"):
  with members:
    - "indexing weighted sequences neat and efficient"
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "efficient index for weighted sequences"
    - "approximate hamming distance in a stream"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "probabilistic threshold indexing for uncertain strings"
    - "space lower bounds for online pattern matching"

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)