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: "why do simple algorithms for triangle enumeration work in the real world"
  Abstract: Listing all triangles is a fundamental graph operation. Triangles can have important interpretations in real-world graphs, especially social and other interaction networks. Despite the lack of provably efficient (linear, or slightly super-linear) worst-case algorithms for this problem, practitioners run simple, efficient heuristics to find all triangles in graphs with millions of vertices. How are these heuristics exploiting the structure of these special graphs to provide major speedups in running time? #R##N#We study one of the most prevalent algorithms used by practitioners. A trivial algorithm enumerates all paths of length $2$, and checks if each such path is incident to a triangle. A good heuristic is to enumerate only those paths of length $2$ where the middle vertex has the lowest degree. It is easily implemented and is empirically known to give remarkable speedups over the trivial algorithm. #R##N#We study the behavior of this algorithm over graphs with heavy-tailed degree distributions, a defining feature of real-world graphs. The erased configuration model (ECM) efficiently generates a graph with asymptotically (almost) any desired degree sequence. We show that the expected running time of this algorithm over the distribution of graphs created by the ECM is controlled by the $\ell_{4/3}$-norm of the degree sequence. Norms of the degree sequence are a measure of the heaviness of the tail, and it is precisely this feature that allows non-trivial speedups of simple triangle enumeration algorithms. As a corollary of our main theorem, we prove expected linear-time performance for degree sequences following a power law with exponent $\alpha \geq 7/3$, and non-trivial speedup whenever $\alpha \in (2,3)$.

Connected through hyperedge HE-1 (induced by source paper "parallel clique counting and peeling algorithms"):
  with members:
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "graphlet decomposition framework algorithms and applications"
    - "densest subgraph in streaming and mapreduce"

Connected through hyperedge HE-2 (induced by source paper "retrieving top weighted triangles in graphs"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "simplicial closure and higher order link prediction"
    - "tiered sampling an efficient method for approximate counting sparse motifs in massive graph streams"
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-3 (induced by source paper "edmot an edge enhancement approach for motif aware community detection"):
  with members:
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-4 (induced by source paper "trusses and trapezes easily interpreted communities in social networks"):
  with members:
    - "truss decomposition in massive networks"

Connected through hyperedge HE-5 (induced by source paper "random walks on simplicial complexes and the normalized hodge 1 laplacian"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "flow smoothing and denoising graph signal processing in the edge space"
    - "simplicial closure and higher order link prediction"
    - "centralities in simplicial complexes"
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "the many facets of community detection in complex networks"
    - "block models and personalized pagerank"
    - "towards spectral sparsification of simplicial complexes based on generalized effective resistance"

Connected through hyperedge HE-6 (induced by source paper "tight bounds for distributed graph computations"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "near optimal clustering in the k machine model"
    - "worst case optimal algorithms for parallel query processing"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "fast distributed pagerank computation"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "the input output complexity of triangle enumeration"

Connected through hyperedge HE-7 (induced by source paper "sublinear time estimation of degree distribution moments the arboricity connection"):
  with members:
    - "sublinear time algorithms for counting star subgraphs with applications to join selectivity estimation"
    - "algorithmic complexity of power law networks"
    - "approximately counting triangles in sublinear time"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-8 (induced by source paper "subgraph counting color coding beyond trees"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "identifying discriminating network motifs in youtube spam"
    - "network analysis of recurring youtube spam campaigns"

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.SI(Social and Information Networks)