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: "probabilistic threshold indexing for uncertain strings"
  Abstract: Strings form a fundamental data type in computer systems. String searching has been extensively studied since the inception of computer science. Increasingly many applications have to deal with imprecise strings or strings with fuzzy information in them. String matching becomes a probabilistic event when a string contains uncertainty, i.e. each position of the string can have different probable characters with associated probability of occurrence for each character. Such uncertain strings are prevalent in various applications such as biological sequence data, event monitoring and automatic ECG annotations. We explore the problem of indexing uncertain strings to support efficient string searching. In this paper we consider two basic problems of string searching, namely substring searching and string listing. In substring searching, the task is to find the occurrences of a deterministic string in an uncertain string. We formulate the string listing problem for uncertain strings, where the objective is to output all the strings from a collection of strings, that contain probable occurrence of a deterministic query string. Indexing solution for both these problems are significantly more challenging for uncertain strings than for deterministic strings. Given a construction time probability value $\tau$, our indexes can be constructed in linear space and supports queries in near optimal time for arbitrary values of probability threshold parameter greater than $\tau$. To the best of our knowledge, this is the first indexing solution for searching in uncertain strings that achieves strong theoretical bound and supports arbitrary values of probability threshold parameter. We also propose an approximate substring search index that can answer substring search queries with an additive error in optimal time. We conduct experiments to evaluate the performance of our indexes.

Connected through hyperedge HE-1 (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"
    - "fast average case pattern matching on weighted sequences"
    - "weighted ancestors in suffix trees"
    - "a linear time algorithm for seeds computation"

Connected through hyperedge HE-2 (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"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-3 (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"
    - "space lower bounds for online pattern matching"

Connected through hyperedge HE-4 (induced by source paper "pattern matching and consensus problems on weighted sequences and profiles"):
  with members:
    - "efficient index for weighted sequences"
    - "streaming k mismatch with error correcting and applications"
    - "polynomial kernels for weighted problems"
    - "fast average case pattern matching on weighted sequences"

Connected through hyperedge HE-5 (induced by source paper "parameterizing pwm and profile matching and knapsack by the feasible weight solutions count"):
  with members:
    - "exact weight subgraphs and the k sum conjecture"

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.DB(Databases)