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: "parameterized matching in the streaming model"
  Abstract: We study the problem of parameterized matching in a stream where we want to output matches between a pattern of length m and the last m symbols of the stream before the next symbol arrives. Parameterized matching is a natural generalisation of exact matching where an arbitrary one-to-one relabelling of pattern symbols is allowed. We show how this problem can be solved in constant time per arriving stream symbol and sublinear, near optimal space with high probability. Our results are surprising and important: it has been shown that almost no streaming pattern matching problems can be solved (not even randomised) in less than Theta(m) space, with exact matching as the only known problem to have a sublinear, near optimal space solution. Here we demonstrate that a similar sublinear, near optimal space solution is achievable for an even more challenging problem. The proof is considerably more complex than that for exact matching.

Connected through hyperedge HE-1 (induced by source paper "the streaming k mismatch problem"):
  with members:
    - "optimal trade offs for pattern matching with k mismatches"
    - "streaming periodicity with mismatches"
    - "streaming k mismatch with error correcting and applications"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-2 (induced by source paper "streaming pattern matching with d wildcards"):
  with members:
    - "the streaming k mismatch problem"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "space lower bounds for online pattern matching"
    - "fast moment estimation in data streams in optimal space"

Connected through hyperedge HE-3 (induced by source paper "parameterized pattern matching succinctly"):
  with members:
    - "linear time construction of compressed text indices in compact space"
    - "succinct dictionary matching with no slowdown"
    - "new algorithms on wavelet trees and applications to information retrieval"

Connected through hyperedge HE-4 (induced by source paper "tight tradeoffs for real time approximation of longest palindromes in streams"):
  with members:
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"

Connected through hyperedge HE-5 (induced by source paper "approximate hamming distance in a stream"):
  with members:
    - "the k mismatch problem revisited"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-6 (induced by source paper "the k mismatch problem revisited"):
  with members:
    - "dictionary matching in a stream"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-7 (induced by source paper "dictionary matching in a stream"):
  with members:
    - "pattern matching in multiple streams"

Connected through hyperedge HE-8 (induced by source paper "online pattern matching for string edit distance with moves"):
  with members:
    - "fully online grammar compression in constant space"
    - "pattern matching in multiple streams"
    - "space lower bounds for online pattern matching"
    - "fully functional static and dynamic succinct trees"

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)