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: "recognizing well parenthesized expressions in the streaming model"
  Abstract: Motivated by a concrete problem and with the goal of understanding the sense in which the complexity of streaming algorithms is related to the complexity of formal languages, we investigate the problem Dyck(s) of checking matching parentheses, with $s$ different types of parenthesis. #R##N#We present a one-pass randomized streaming algorithm for Dyck(2) with space $\Order(\sqrt{n}\log n)$, time per letter $\polylog (n)$, and one-sided error. We prove that this one-pass algorithm is optimal, up to a $\polylog n$ factor, even when two-sided error is allowed. For the lower bound, we prove a direct sum result on hard instances by following the "information cost" approach, but with a few twists. Indeed, we play a subtle game between public and private coins. This mixture between public and private coins results from a balancing act between the direct sum result and a combinatorial lower bound for the base case. #R##N#Surprisingly, the space requirement shrinks drastically if we have access to the input stream in reverse. We present a two-pass randomized streaming algorithm for Dyck(2) with space $\Order((\log n)^2)$, time $\polylog (n)$ and one-sided error, where the second pass is in the reverse direction. Both algorithms can be extended to Dyck(s) since this problem is reducible to Dyck(2) for a suitable notion of reduction in the streaming model.

Connected through hyperedge HE-1 (induced by source paper "memory augmented recurrent neural networks can learn generalized dyck languages"):
  with members:
    - "theoretical limitations of self attention in neural sequence models"
    - "sequential neural networks as automata"
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "attention is all you need"
    - "lie access neural turing machine"
    - "learning operations on a stack with neural turing machines"

Connected through hyperedge HE-2 (induced by source paper "lstm networks can perform dynamic counting"):
  with members:
    - "context free transductions with neural stacks"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "evaluating the ability of lstms to learn context free grammars"
    - "learning operations on a stack with neural turing machines"
    - "learning to transduce with unbounded memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"

Connected through hyperedge HE-3 (induced by source paper "evaluating the ability of lstms to learn context free grammars"):
  with members:
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "deep learning with dynamic computation graphs"
    - "don t decay the learning rate increase the batch size"
    - "what do neural machine translation models learn about morphology"
    - "recurrent neural network grammars"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "end to end memory networks"

Connected through hyperedge HE-4 (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"
    - "streaming algorithms for language recognition problems"

Connected through hyperedge HE-5 (induced by source paper "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"):
  with members:
    - "clustered integer 3sum via additive combinatorics"
    - "hardness of rna folding problem with four symbols"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "powers of tensors and fast matrix multiplication"
    - "language edit distance maximum likelihood parsing of stochastic grammars faster algorithms connection to fundamental graph problems"
    - "an error correcting parser for context free grammars that takes less than cubic time"
    - "faster all pairs shortest paths via circuit complexity"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"

Connected through hyperedge HE-6 (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"
    - "streaming property testing of visibly pushdown languages"
    - "language edit distance maximum likelihood parsing of stochastic grammars faster algorithms connection to fundamental graph problems"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-7 (induced by source paper "streaming communication protocols"):
  with members:
    - "maximum matching in turnstile streams"
    - "improved algorithms for distributed entropy monitoring"
    - "better bounds for matchings in the streaming model"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "tight bounds for distributed functional monitoring"
    - "continuous monitoring of distributed data streams over a time based sliding window"
    - "optimal tracking of distributed heavy hitters and quantiles"
    - "on the complexity of processing massive unordered distributed data"

Connected through hyperedge HE-8 (induced by source paper "streaming property testing of visibly pushdown languages"):
  with members:
    - "validating xml documents in the streaming model with external memory"
    - "first order and temporal logics for nested words"
    - "logics for unranked trees an overview"

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)