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 algorithms for language recognition problems"
  Abstract: We study the complexity of the following problems in the streaming model. #R##N#Membership testing for \DLIN We show that every language in \DLIN\ can be recognised by a randomized one-pass $O(\log n)$ space algorithm with inverse polynomial one-sided error, and by a deterministic p-pass $O(n/p)$ space algorithm. We show that these algorithms are optimal. #R##N#Membership testing for \LL$(k)$ For languages generated by \LL$(k)$ grammars with a bound of $r$ on the number of nonterminals at any stage in the left-most derivation, we show that membership can be tested by a randomized one-pass $O(r\log n)$ space algorithm with inverse polynomial (in $n$) one-sided error. #R##N#Membership testing for \DCFL We show that randomized algorithms as efficient as the ones described above for \DLIN\ and $\LL(k)$ (which are subclasses of \DCFL) cannot exist for all of \DCFL: there is a language in \VPL\ (a subclass of \DCFL) for which any randomized p-pass algorithm with error bounded by $\epsilon < 1/2$ must use $\Omega(n/p)$ space. #R##N#Degree sequence problem We study the problem of determining, given a sequence $d_1, d_2,..., d_n$ and a graph $G$, whether the degree sequence of $G$ is precisely $d_1, d_2,..., d_n$. We give a randomized one-pass $O(\log n)$ space algorithm with inverse polynomial one-sided error probability. We show that our algorithms are optimal. #R##N#Our randomized algorithms are based on the recent work of Magniez et al. \cite{MMN09}; our lower bounds are obtained by considering related communication complexity problems.

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"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"

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"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"
    - "recognizing well parenthesized expressions in the streaming model"

Connected through hyperedge HE-3 (induced by source paper "extension complexity of formal languages"):
  with members:
    - "approximate constraint satisfaction requires large lp relaxations"
    - "on the existence of 0 1 polytopes with high semidefinite extension complexity"
    - "the matching polytope has exponential extension complexity"
    - "approximation limits of linear programs beyond hierarchies"

Connected through hyperedge HE-4 (induced by source paper "streaming algorithms for recognizing nearly well parenthesized expressions"):
  with members:
    - "information cost tradeoffs for augmented index and streaming language recognition"
    - "data stream algorithms for codeword testing"
    - "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)