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: "l p pattern matching in a stream"
  Abstract: We consider the problem of computing distance between a pattern of length $n$ and all $n$-length subwords of a text in the streaming model. #R##N#In the streaming setting, only the Hamming distance ($L_0$) has been studied. It is known that computing the Hamming distance between a pattern and a streaming text exactly requires $\Omega(n)$ space. Therefore, to develop sublinear-space solutions, one must relax their requirements. One possibility to do so is to compute only the distances bounded by a threshold $k$, see~[SODA'19, Clifford, Kociumaka, Porat]. The motivation for this variant of this problem is that we are interested in subwords of the text that are similar to the pattern, i.e. in subwords such that the distance between them and the pattern is relatively small. #R##N#On the other hand, the main application of the streaming setting is processing large-scale data, such as biological data. Recent advances in hardware technology allow generating such data at a very high speed, but unfortunately, the produced data may contain about 10\% of noise~[Biol. Direct.'07, Klebanov and Yakovlev]. To analyse such data, it is not sufficient to consider small distances only. A possible workaround for this issue is $(1\pm\varepsilon)$-approximation. This line of research was initiated in [ICALP'16, Clifford and Starikovskaya] who gave a $(1\pm\varepsilon)$-approximation algorithm with space $\widetilde{O}(\varepsilon^{-5}\sqrt{n})$. #R##N#In this work, we show a suite of new streaming algorithms for computing the Hamming, $L_1$, $L_2$ and general $L_p$ ($0 < p \le 2$) distances between the pattern and the text. Our results significantly extend over the previous result in this setting. In particular, for the Hamming distance case and for the $L_p$ distance when $0 < p \le 1$ we show a streaming algorithm that uses $\widetilde{O}(\varepsilon^{-2}\sqrt{n})$ space for polynomial-size alphabets.

Connected through hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances"):
  with members:
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "the streaming k mismatch problem"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"

Connected through hyperedge HE-2 (induced by source paper "approximating text to pattern distance via dimensionality reduction"):
  with members:
    - "approximating approximate pattern matching"
    - "the streaming k mismatch problem"
    - "approximate hamming distance in a stream"
    - "sparser johnson lindenstrauss transforms"

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)