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: "weighted ancestors in suffix trees"
  Abstract: The classical, ubiquitous, predecessor problem is to construct a data structure for a set of integers that supports fast predecessor queries. Its generalization to weighted trees, a.k.a. the weighted ancestor problem, has been extensively explored and successfully reduced to the predecessor problem. It is known that any solution for both problems with an input set from a polynomially bounded universe that preprocesses a weighted tree in O(n polylog(n)) space requires \Omega(loglogn) query time. Perhaps the most important and frequent application of the weighted ancestors problem is for suffix trees. It has been a long-standing open question whether the weighted ancestors problem has better bounds for suffix trees. We answer this question positively: we show that a suffix tree built for a text w[1..n] can be preprocessed using O(n) extra space, so that queries can be answered in O(1) time. Thus we improve the running times of several applications. Our improvement is based on a number of data structure tools and a periodicity-based insight into the combinatorial structure of a suffix tree.

Connected through hyperedge HE-1 (induced by source paper "algorithms for anti powers in strings"):
  with members:
    - "anti powers in infinite words"

Connected through hyperedge HE-2 (induced by source paper "indexing weighted sequences neat and efficient"):
  with members:
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "efficient index for weighted sequences"
    - "probabilistic threshold indexing for uncertain strings"
    - "fast average case pattern matching on weighted sequences"
    - "a linear time algorithm for seeds computation"

Connected through hyperedge HE-3 (induced by source paper "detecting one variable patterns"):
  with members:
    - "eertree an efficient data structure for processing palindromes in strings"
    - "online detection of repetitions with backtracking"
    - "internal pattern matching queries in a text and applications"
    - "a minimal periods algorithm with applications"

Connected through hyperedge HE-4 (induced by source paper "optimal computation of avoided words"):
  with members:
    - "space efficient detection of unusual words"
    - "linear time computation of minimal absent words using suffix array"

Connected through hyperedge HE-5 (induced by source paper "wavelet trees meet suffix trees"):
  with members:
    - "sorted range reporting revisited"
    - "internal pattern matching queries in a text and applications"
    - "the wavelet trie maintaining an indexed sequence of strings in compressed space"

Connected through hyperedge HE-6 (induced by source paper "on the average case complexity of pattern matching with wildcards"):
  with members:
    - "space efficient string indexing for wildcard pattern matching"
    - "succincter text indexing with wildcards"

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)