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: "finding small patterns in permutations in linear time"
  Abstract: Given two permutations $\sigma$ and $\pi$, the \textsc{Permutation Pattern} problem asks if $\sigma$ is a subpattern of $\pi$. We show that the problem can be solved in time $2^{O(\ell^2\log \ell)}\cdot n$, where $\ell=|\sigma|$ and $n=|\pi|$. In other words, the problem is fixed-parameter tractable parameterized by the size of the subpattern to be found. #R##N#We introduce a novel type of decompositions for permutations and a corresponding width measure. We present a linear-time algorithm that either finds $\sigma$ as a subpattern of $\pi$, or finds a decomposition of $\pi$ whose width is bounded by a function of $|\sigma|$. Then we show how to solve the \textsc{Permutation Pattern} problem in linear time if a bounded-width decomposition is given in the input.

Connected through hyperedge HE-1 (induced by source paper "optimal adaptive detection of monotone patterns"):
  with members:
    - "finding and counting permutations via csps"
    - "finding monotone patterns in sublinear time"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-2 (induced by source paper "finding monotone patterns in sublinear time"):
  with members:
    - "faster and simpler algorithms for finding large patterns in permutations"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"

Connected through hyperedge HE-3 (induced by source paper "counting small permutation patterns"):
  with members:
    - "faster and simpler algorithms for finding large patterns in permutations"
    - "finding and counting permutations via csps"
    - "improved bounds for testing forbidden order patterns"
    - "the computational landscape of permutation patterns"
    - "a fast algorithm for permutation pattern matching based on alternating runs"
    - "hereditary properties of permutations are strongly testable"
    - "orthogonal range searching on the ram revisited"

Connected through hyperedge HE-4 (induced by source paper "finding and counting permutations via csps"):
  with members:
    - "improved bounds for testing forbidden order patterns"
    - "pattern avoiding access in binary search trees"
    - "hitting set for hypergraphs of low vc dimension"
    - "the computational landscape of permutation patterns"
    - "a fast algorithm for permutation pattern matching based on alternating runs"

Connected through hyperedge HE-5 (induced by source paper "faster and simpler algorithms for finding large patterns in permutations"):
  with members:
    - "improved bounds for testing forbidden order patterns"
    - "pattern avoiding access in binary search trees"
    - "the computational landscape of permutation patterns"
    - "a fast algorithm for permutation pattern matching based on alternating runs"

Connected through hyperedge HE-6 (induced by source paper "combinatorial generation via permutation languages i fundamentals"):
  with members:
    - "faster and simpler algorithms for finding large patterns in permutations"
    - "topological drawings of complete bipartite graphs"

Connected through hyperedge HE-7 (induced by source paper "smooth heaps and a dual view of self adjusting data structures"):
  with members:
    - "weighted dynamic finger in binary search trees"
    - "improved bounds for multipass pairing heaps and path balanced binary search trees"
    - "pairing heaps the forward variant"
    - "the landscape of bounds for binary search trees"
    - "pattern avoiding access in binary search trees"
    - "greedy is an almost optimal deque"
    - "a back to basics empirical study of priority queues"
    - "a tight lower bound for decrease key in the pure heap model"

Connected through hyperedge HE-8 (induced by source paper "improved bounds for testing forbidden order patterns"):
  with members:
    - "testing k monotonicity"
    - "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"
    - "transitive closure spanners"

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)