human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "approximating approximate pattern matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating text to pattern distance via dimensionality reduction")
  Member "the streaming k mismatch problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating text to pattern hamming distances")
  Member "approximate hamming distance in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating text to pattern hamming distances")
  Member "the k mismatch problem revisited":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating text to pattern hamming distances")
  Member "how to catch l 2 heavy hitters on sliding windows":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "numerical linear algebra in the sliding window model")
  Member "an optimal lower bound on the communication complexity of gap hamming distance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")

Question: Please tell me which class the source paper 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)