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 "detecting and counting small patterns in planar graphs in subexponential parameterized time"):
  Abstract: We present an algorithm that takes as input an $n$-vertex planar graph $G$ and a $k$-vertex pattern graph $P$, and computes the number of (induced) copies of $P$ in $G$ in $2^{O(k/\log k)}n^{O(1)}$ time. If $P$ is a matching, independent set, or connected bounded maximum degree graph, the runtime reduces to $2^{\tilde{O}(\sqrt{k})}n^{O(1)}$. #R##N#While our algorithm counts all copies of $P$, it also improves the fastest algorithms that only detect copies of $P$. Before our work, no $2^{O(k/\log k)}n^{O(1)}$ time algorithms for detecting unrestricted patterns $P$ were known, and by a result of Bodlaender et al. [ICALP 2016] a $2^{o(k/\log k)}n^{O(1)}$ time algorithm would violate the Exponential Time Hypothesis (ETH). Furthermore, it was only known how to detect copies of a fixed connected bounded maximum degree pattern $P$ in $2^{\tilde{O}(\sqrt{k})}n^{O(1)}$ time probabilistically. For counting problems, it was a repeatedly asked open question whether $2^{o(k)}n^{O(1)}$ time algorithms exist that count even special patterns such as independent sets, matchings and paths in planar graphs. The above results resolve this question in a strong sense by giving algorithms for counting versions of problems with running times equal to the ETH lower bounds for their decision versions. #R##N#Generally speaking, our algorithm counts copies of $P$ in time proportional to its number of non-isomorphic separations of order $\tilde{O}(\sqrt{k})$. The algorithm introduces a new recursive approach to construct families of balanced cycle separators in planar graphs that have limited overlap inspired by methods from Fomin et al. [FOCS 2016], a new `efficient' inclusion-exclusion based argument and uses methods from Bodlaender et al. [ICALP 2016].

Includes members:
  Member "homomorphisms are a good basis for counting small subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "local wl invariance and hidden shades of regularity")
  Member "computing the number of induced copies of a fixed graph in a bounded degree graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "statistical physics approaches to unique games")
  Member "subexponential parameterized algorithms for planar and apex minor free graphs via low treewidth pattern covering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tight bounds for planar strongly connected steiner subgraph with fixed number of terminals and extensions")
  Member "counting matchings with k unmatched vertices in planar graphs":
    also connected through other hyperedges:
      (none)
  Member "approximating the partition function of planar two state spin systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region")
  Member "on triangulating k outerplanar graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pixel and voxel representations of graphs")
  Member "approximate counting via correlation decay on planar graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perfect sampling from spatial mixing")
  Member "beyond bidimensionality parameterized subexponential algorithms on directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "subexponential parameterized algorithms for planar and apex minor free graphs via low treewidth pattern covering")

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)