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: "tight cell probe bounds for online hamming distance computation"
  Abstract: We show tight bounds for online Hamming distance computation in the cell-probe model with word size w. The task is to output the Hamming distance between a fixed string of length n and the last n symbols of a stream. We give a lower bound of Omega((d/w)*log n) time on average per output, where d is the number of bits needed to represent an input symbol. We argue that this bound is tight within the model. The lower bound holds under randomisation and amortisation.

Connected through hyperedge HE-1 (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"
    - "cell probe bounds for online edit distance and other pattern matching problems"
    - "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 "cell probe lower bounds for dynamic problems via a new communication model"):
  with members:
    - "new unconditional hardness results for dynamic and online problems"
    - "cell probe bounds for online edit distance and other pattern matching problems"
    - "faster all pairs shortest paths via circuit 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-3 (induced by source paper "the complexity of computation in bit streams"):
  with members:
    - "cell probe bounds for online edit distance and other pattern matching problems"
    - "the cell probe complexity of dynamic range counting"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-4 (induced by source paper "time lower bounds for nonadaptive turnstile streaming algorithms"):
  with members:
    - "cell probe bounds for online edit distance and other pattern matching problems"
    - "parameterized streaming algorithms for vertex cover"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "cell probe bounds for online edit distance and other pattern matching problems"):
  with members:
    - "the cell probe complexity of dynamic range counting"
    - "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)