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: "indexing weighted sequences neat and efficient"
  Abstract: In a \emph{weighted sequence}, for every position of the sequence and every letter of the alphabet a probability of occurrence of this letter at this position is specified. Weighted sequences are commonly used to represent imprecise or uncertain data, for example, in molecular biology where they are known under the name of Position-Weight Matrices. Given a probability threshold $\frac1z$, we say that a string $P$ of length $m$ occurs in a weighted sequence $X$ at position $i$ if the product of probabilities of the letters of $P$ at positions $i,\ldots,i+m-1$ in $X$ is at least $\frac1z$. In this article, we consider an \emph{indexing} variant of the problem, in which we are to preprocess a weighted sequence to answer multiple pattern matching queries. We present an $O(nz)$-time construction of an $O(nz)$-sized index for a weighted sequence of length $n$ over a constant-sized alphabet that answers pattern matching queries in optimal, $O(m+Occ)$ time, where $Occ$ is the number of occurrences reported. The cornerstone of our data structure is a novel construction of a family of $\lfloor z \rfloor$ special strings that carries the information about all the strings that occur in the weighted sequence with a sufficient probability. We obtain a weighted index with the same complexities as in the most efficient previously known index by Barton et al. (CPM 2016), but our construction is significantly simpler. The most complex algorithmic tool required in the basic form of our index is the suffix tree which we use to develop a new, more straightforward index for the so-called property matching problem. We provide an implementation of our data structure. Our construction allows us also to obtain a significant improvement over the complexities of the approximate variant of the weighted index presented by Biswas et al. (EDBT 2016) and an improvement of the space complexity of their general index.

Connected through hyperedge HE-1 (induced by source paper "experimental evaluation of algorithms for computing quasiperiods"):
  with members:
    - "two strings at hamming distance 1 cannot be both quasiperiodic"
    - "efficient index for weighted sequences"
    - "computing covers using prefix tables"
    - "covering problems for partial words and for indeterminate strings"
    - "a linear time algorithm for seeds computation"

Connected through hyperedge HE-2 (induced by source paper "weighted shortest common supersequence problem revisited"):
  with members:
    - "longest common subsequence on weighted sequences"
    - "faster space efficient algorithms for subset sum k sum and related problems"
    - "pattern matching and consensus problems on weighted sequences and profiles"
    - "efficient index for weighted sequences"
    - "streaming k mismatch with error correcting and applications"

Connected through hyperedge HE-3 (induced by source paper "longest property preserved common factor"):
  with members:
    - "linear time algorithm for long lcf with k mismatches"
    - "the runs theorem"
    - "a hardness result and new algorithm for the longest common palindromic subsequence problem"
    - "sublinear space algorithms for the longest common substring problem"
    - "computing a longest common palindromic subsequence"

Connected through hyperedge HE-4 (induced by source paper "alignment free sequence comparison using absent words"):
  with members:
    - "minimal forbidden factors of circular words"
    - "linear time sequence comparison using minimal absent words applications"
    - "linear time computation of minimal absent words using suffix array"
    - "inducing the lcp array"

Connected through hyperedge HE-5 (induced by source paper "streaming k mismatch with error correcting and applications"):
  with members:
    - "the streaming k mismatch problem"
    - "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"
    - "space lower bounds for online pattern matching"

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)