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: "streaming property testing of visibly pushdown languages"
  Abstract: In the context of language recognition, we demonstrate the superiority of streaming property testers against streaming algorithms and property testers, when they are not combined. Initiated by Feigenbaum et al., a streaming property tester is a streaming algorithm recognizing a language under the property testing approximation: it must distinguish inputs of the language from those that are $\varepsilon$-far from it, while using the smallest possible memory (rather than limiting its number of input queries). #R##N#Our main result is a streaming $\varepsilon$-property tester for visibly pushdown languages (VPL) with one-sided error using memory space $\mathrm{poly}((\log n) / \varepsilon)$. #R##N#This constructions relies on a (non-streaming) property tester for weighted regular languages based on a previous tester by Alon et al. We provide a simple application of this tester for streaming testing special cases of instances of VPL that are already hard for both streaming algorithms and property testers. #R##N#Our main algorithm is a combination of an original simulation of visibly pushdown automata using a stack with small height but possible items of linear size. In a second step, those items are replaced by small sketches. Those sketches relies on a notion of suffix-sampling we introduce. This sampling is the key idea connecting our streaming tester algorithm to property testers.

Connected through hyperedge HE-1 (induced by source paper "sliding window property testing for regular languages"):
  with members:
    - "visibly pushdown languages over sliding windows"
    - "randomized sliding window algorithms for regular languages"
    - "automata theory on sliding windows"
    - "streaming pattern matching with d wildcards"
    - "approximate hamming distance in a stream"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"

Connected through hyperedge HE-2 (induced by source paper "visibly pushdown languages over sliding windows"):
  with members:
    - "automata theory on sliding windows"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "streaming algorithms for language recognition problems"
    - "recognizing well parenthesized expressions in the streaming model"

Connected through hyperedge HE-3 (induced by source paper "streamability of nested word transductions"):
  with members:
    - "randomized sliding window algorithms for regular languages"
    - "untwisting two way transducers in elementary time"
    - "two way visibly pushdown automata and transducers"
    - "from two way to one way finite state transducers"
    - "streaming tree transducers"
    - "on functionality of visibly pushdown transducers"
    - "validating xml documents in the streaming model with external memory"
    - "tight lower bounds for query processing on streaming and external memory data"

Connected through hyperedge HE-4 (induced by source paper "automata theory on sliding windows"):
  with members:
    - "approximate hamming distance in a stream"
    - "dictionary matching in a stream"
    - "the k mismatch problem revisited"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"
    - "validating xml documents in the streaming model with external memory"

Connected through hyperedge HE-5 (induced by source paper "improved bounds for testing dyck languages"):
  with members:
    - "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"
    - "language edit distance maximum likelihood parsing of stochastic grammars faster algorithms connection to fundamental graph problems"
    - "testing closeness of discrete distributions"
    - "recognizing well parenthesized expressions 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)