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: "cell probe bounds for online edit distance and other pattern matching problems"
  Abstract: We give cell-probe bounds for the computation of edit distance, Hamming distance, convolution and longest common subsequence in a stream. In this model, a fixed string of $n$ symbols is given and one $\delta$-bit symbol arrives at a time in a stream. After each symbol arrives, the distance between the fixed string and a suffix of most recent symbols of the stream is reported. The cell-probe model is perhaps the strongest model of computation for showing data structure lower bounds, subsuming in particular the popular word-RAM model. #R##N#* We first give an $\Omega((\delta \log n)/(w+\log\log n))$ lower bound for the time to give each output for both online Hamming distance and convolution, where $w$ is the word size. This bound relies on a new encoding scheme and for the first time holds even when $w$ is as small as a single bit. #R##N#* We then consider the online edit distance and longest common subsequence problems in the bit-probe model ($w=1$) with a constant sized input alphabet. We give a lower bound of $\Omega(\sqrt{\log n}/(\log\log n)^{3/2})$ which applies for both problems. This second set of results relies both on our new encoding scheme as well as a carefully constructed hard distribution. #R##N#* Finally, for the online edit distance problem we show that there is an $O((\log n)^2/w)$ upper bound in the cell-probe model. This bound gives a contrast to our new lower bound and also establishes an exponential gap between the known cell-probe and RAM model complexities.

Connected through hyperedge HE-1 (induced by source paper "amortized dynamic cell probe lower bounds from four party communication"):
  with members:
    - "cell probe lower bounds for dynamic problems via a new communication model"
    - "new unconditional hardness results for dynamic and online problems"
    - "on arthur merlin games in communication complexity"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-2 (induced by source paper "new unconditional hardness results for dynamic and online problems"):
  with members:
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "tight cell probe bounds for online hamming distance computation"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-3 (induced by source paper "cell probe lower bounds for dynamic problems via a new communication model"):
  with members:
    - "new unconditional hardness results for dynamic and online problems"
    - "faster all pairs shortest paths via circuit complexity"
    - "tight cell probe bounds for online hamming distance computation"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-4 (induced by source paper "the complexity of computation in bit streams"):
  with members:
    - "tight cell probe bounds for online hamming distance computation"
    - "the cell probe complexity of dynamic range counting"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-5 (induced by source paper "time lower bounds for nonadaptive turnstile streaming algorithms"):
  with members:
    - "parameterized streaming algorithms for vertex cover"
    - "tight cell probe bounds for online hamming distance computation"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "fast moment estimation in data streams in optimal space"
    - "lower bounds on near neighbor search via metric expansion"
    - "sketching and streaming entropy via approximation theory"
    - "cr precis a deterministic summary structure for update data streams"
    - "logarithmic lower bounds in the cell probe model"

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)