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: "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
  Abstract: In the subgraph counting problem, we are given a input graph $G(V, E)$ and a target graph $H$; the goal is to estimate the number of occurrences of $H$ in $G$. Our focus here is on designing sublinear-time algorithms for approximately counting occurrences of $H$ in $G$ in the setting where the algorithm is given query access to $G$. This problem has been studied in several recent papers which primarily focused on specific families of graphs $H$ such as triangles, cliques, and stars. However, not much is known about approximate counting of arbitrary graphs $H$. This is in sharp contrast to the closely related subgraph enumeration problem that has received significant attention in the database community as the database join problem. The AGM bound shows that the maximum number of occurrences of any arbitrary subgraph $H$ in a graph $G$ with $m$ edges is $O(m^{\rho(H)})$, where $\rho(H)$ is the fractional edge-cover of $H$, and enumeration algorithms with matching runtime are known for any $H$. #R##N#We bridge this gap between subgraph counting and subgraph enumeration by designing a sublinear-time algorithm that can estimate the number of any arbitrary subgraph $H$ in $G$, denoted by $\#H$, to within a $(1\pm \epsilon)$-approximation w.h.p. in $O(\frac{m^{\rho(H)}}{\#H}) \cdot poly(\log{n},1/\epsilon)$ time. Our algorithm is allowed the standard set of queries for general graphs, namely degree queries, pair queries and neighbor queries, plus an additional edge-sample query that returns an edge chosen uniformly at random. The performance of our algorithm matches those of Eden et.al. [FOCS 2015, STOC 2018] for counting triangles and cliques and extend them to all choices of subgraph $H$ under the additional assumption of edge-sample queries. We further show that our algorithm works for the more general database join size estimation problem and prove a matching lower bound for this problem.

Connected through hyperedge HE-1 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six"):
  with members:
    - "homomorphisms are a good basis for counting small subgraphs"
    - "on approximating the number of k cliques in sublinear time"
    - "combinatorial algorithm for counting small induced graphs and orbits"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "approximately counting triangles in sublinear time"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"
    - "counting triangles in massive graphs with mapreduce"

Connected through hyperedge HE-2 (induced by source paper "nearly optimal edge estimation with independent set queries"):
  with members:
    - "fiding forbidden minors in sublinear time a o n 1 2 o 1 query one sided tester for minor closed properties on bounded degree graphs"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time algorithms for counting star subgraphs with applications to join selectivity estimation"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-3 (induced by source paper "the arboricity captures the complexity of sampling edges"):
  with members:
    - "faster sublinear approximations of k cliques for low arboricity graphs"
    - "lower bounds for approximating graph parameters via communication complexity"
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximately counting triangles in sublinear time"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-4 (induced by source paper "a lower bound on cycle finding in sparse digraphs"):
  with members:
    - "fiding forbidden minors in sublinear time a o n 1 2 o 1 query one sided tester for minor closed properties on bounded degree graphs"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "approximately counting triangles in sublinear time"
    - "property testing in sparse directed graphs 3 star freeness and connectivity"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

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)