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: "dictionary matching in a stream"
  Abstract: We consider the problem of dictionary matching in a stream. Given a set of strings, known as a dictionary, and a stream of characters arriving one at a time, the task is to report each time some string in our dictionary occurs in the stream. We present a randomised algorithm which takes O(log log(k + m)) time per arriving character and uses O(k log m) words of space, where k is the number of strings in the dictionary and m is the length of the longest string in the dictionary.

Connected through hyperedge HE-1 (induced by source paper "sliding window property testing for regular languages"):
  with members:
    - "visibly pushdown languages over sliding windows"
    - "randomized sliding window algorithms for regular languages"
    - "automata theory on sliding windows"
    - "streaming pattern matching with d wildcards"
    - "approximate hamming distance in a stream"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"

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"
    - "streaming k mismatch with error correcting and applications"
    - "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 "visibly pushdown languages over sliding windows"):
  with members:
    - "automata theory on sliding windows"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"
    - "streaming algorithms for language recognition problems"
    - "recognizing well parenthesized expressions in the streaming model"

Connected through hyperedge HE-4 (induced by source paper "compressed multiple pattern matching"):
  with members:
    - "greedy shortest common superstring approximation in compact space"
    - "engineering small space dictionary matching"
    - "fixed block compression boosting in fm indexes"
    - "succinct dictionary matching with no slowdown"
    - "large alphabets and incompressibility"

Connected through hyperedge HE-5 (induced by source paper "automata theory on sliding windows"):
  with members:
    - "approximate hamming distance in a stream"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"
    - "validating xml documents in the streaming model with external memory"

Connected through hyperedge HE-6 (induced by source paper "streaming periodicity with mismatches"):
  with members:
    - "streaming pattern matching with d wildcards"
    - "tight tradeoffs for real time approximation of longest palindromes in streams"
    - "streaming k mismatch with data recovery and applications"
    - "the k mismatch problem revisited"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-7 (induced by source paper "streaming pattern matching with d wildcards"):
  with members:
    - "the streaming k mismatch problem"
    - "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-8 (induced by source paper "streaming k mismatch with error correcting and applications"):
  with members:
    - "the streaming k mismatch problem"
    - "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"
    - "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)