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: "sublinear time estimation of degree distribution moments the degeneracy connection"
  Abstract: We revisit the classic problem of estimating the degree distribution moments of an undirected graph. Consider an undirected graph $G=(V,E)$ with $n$ vertices, and define (for $s > 0$) $\mu_s = \frac{1}{n}\cdot\sum_{v \in V} d^s_v$. Our aim is to estimate $\mu_s$ within a multiplicative error of $(1+\epsilon)$ (for a given approximation parameter $\epsilon>0$) in sublinear time. We consider the sparse graph model that allows access to: uniform random vertices, queries for the degree of any vertex, and queries for a neighbor of any vertex. For the case of $s=1$ (the average degree), $\widetilde{O}(\sqrt{n})$ queries suffice for any constant $\epsilon$ (Feige, SICOMP 06 and Goldreich-Ron, RSA 08). Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral $s > 0$, by designing an algorithms that performs $\widetilde{O}(n^{1-1/(s+1)})$ queries. #R##N#We design a new, significantly simpler algorithm for this problem. In the worst-case, it exactly matches the bounds of Gonen-Ron-Shavitt, and has a much simpler proof. More importantly, the running time of this algorithm is connected to the degeneracy of $G$. This is (essentially) the maximum density of an induced subgraph. For the family of graphs with degeneracy at most $\alpha$, it has a query complexity of $\widetilde{O}\left(\frac{n^{1-1/s}}{\mu^{1/s}_s} \Big(\alpha^{1/s} + \min\{\alpha,\mu^{1/s}_s\}\Big)\right) = \widetilde{O}(n^{1-1/s}\alpha/\mu^{1/s}_s)$. Thus, for the class of bounded degeneracy graphs (which includes all minor closed families and preferential attachment graphs), we can estimate the average degree in $\widetilde{O}(1)$ queries, and can estimate the variance of the degree distribution in $\widetilde{O}(\sqrt{n})$ queries. This is a major improvement over the previous worst-case bounds. Our key insight is in designing an estimator for $\mu_s$ that has low variance when $G$ does not have large dense subgraphs.

Connected through hyperedge HE-1 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints"):
  with members:
    - "on a generalization of iterated and randomized rounding"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "approximation friendly discrepancy rounding"
    - "matroid and knapsack center problems"
    - "approximating k median via pseudo approximation"
    - "fault tolerant facility location a randomized dependent lp rounding algorithm"

Connected through hyperedge HE-2 (induced by source paper "the arboricity captures the complexity of sampling edges"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "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"
    - "approximately counting triangles in sublinear time"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-3 (induced by source paper "testing graph clusterability algorithms and lower bounds"):
  with members:
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "estimating graph parameters from random order streams"
    - "on sampling edges almost uniformly"
    - "testing cluster structure of graphs"
    - "approximately counting triangles in sublinear time"
    - "a simpler sublinear algorithm for approximating the triangle count"
    - "partitioning into expanders"

Connected through hyperedge HE-4 (induced by source paper "provable and practical approximations for the degree distribution using sublinear graph samples"):
  with members:
    - "measuring and modeling bipartite graphs with community structure"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "approximately counting triangles in sublinear time"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "graph sample and hold a framework for big graph analytics"
    - "network sampling from static to streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-5 (induced by source paper "on approximating the number of k cliques in sublinear time"):
  with members:
    - "lower bounds for approximating graph parameters via communication complexity"
    - "on sampling edges almost uniformly"
    - "clique counting in mapreduce algorithms and experiments"
    - "approximately counting triangles in sublinear time"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "parallel triangle counting in massive streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-6 (induced by source paper "lower bounds for approximating graph parameters via communication complexity"):
  with members:
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "approximately counting triangles in sublinear time"
    - "a stable marriage requires communication"

Connected through hyperedge HE-7 (induced by source paper "on sampling edges almost uniformly"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "estimating and sampling graphs with multidimensional random walks"

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)