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: "space lower bounds for online pattern matching"
  Abstract: We present space lower bounds for online pattern matching under a number of different distance measures. Given a pattern of length m and a text that arrives one character at a time, the online pattern matching problem is to report the distance between the pattern and a sliding window of the text as soon as the new character arrives. We require that the correct answer is given at each position with constant probability. We give Omega(m) bit space lower bounds for L_1, L_2, L_\infty, Hamming, edit and swap distances as well as for any algorithm that computes the cross-correlation/convolution. We then show a dichotomy between distance functions that have wildcard-like properties and those that do not. In the former case which includes, as an example, pattern matching with character classes, we give Omega(m) bit space lower bounds. For other distance functions, we show that there exist space bounds of Omega(log m) and O(log^2 m) bits. Finally we discuss space lower bounds for non-binary inputs and show how in some cases they can be improved.

Connected through hyperedge HE-1 (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"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "space efficient string indexing for wildcard pattern matching"

Connected through hyperedge HE-2 (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"
    - "parameterized matching in the streaming model"

Connected through hyperedge HE-3 (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"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"

Connected through hyperedge HE-4 (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"
    - "parameterized matching in the streaming model"
    - "fast moment estimation in data streams in optimal space"

Connected through hyperedge HE-5 (induced by source paper "streaming for aibohphobes longest palindrome with mismatches"):
  with members:
    - "tight tradeoffs for real time approximation of longest palindromes in streams"
    - "edit distance sketching streaming and document exchange"
    - "the k mismatch problem revisited"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-6 (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"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "probabilistic threshold indexing for uncertain strings"

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

Connected through hyperedge HE-8 (induced by source paper "the k mismatch problem revisited"):
  with members:
    - "dictionary matching in a stream"
    - "parameterized matching in the streaming 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)