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: "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
  Abstract: Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting results for 4-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes. Indeed, even a highly tuned enumeration code takes more than a day on a graph with millions of edges. Most previous work that runs for truly massive graphs employ clusters and massive parallelization. #R##N#We provide a sampling algorithm that provably and accurately approximates the frequencies of all 4-vertex pattern subgraphs. Our algorithm is based on a novel technique of 3-path sampling and a special pruning scheme to decrease the variance in estimates. We provide theoretical proofs for the accuracy of our algorithm, and give formal bounds for the error and confidence of our estimates. We perform a detailed empirical study and show that our algorithm provides estimates within 1% relative error for all subpatterns (over a large class of test graphs), while being orders of magnitude faster than enumeration and other sampling based algorithms. Our algorithm takes less than a minute (on a single commodity machine) to process an Orkut social network with 300 million edges.

Connected through hyperedge HE-1 (induced by source paper "the power of pivoting for exact clique counting"):
  with members:
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "clique counting in mapreduce algorithms and experiments"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"

Connected through hyperedge HE-2 (induced by source paper "a fast counting method for 6 motifs with low connectivity"):
  with members:
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-3 (induced by source paper "improved mixing time for k subgraph sampling"):
  with members:
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "efficiently estimating motif statistics of large networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-4 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "butterfly counting in bipartite networks"
    - "estimation of graphlet statistics"
    - "combinatorial algorithm for counting small induced graphs and orbits"

Connected through hyperedge HE-5 (induced by source paper "efficiently counting vertex orbits of all 5 vertex subgraphs by evoke"):
  with members:
    - "heterogeneous network motifs"
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-6 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "subgraph networks with application to structural feature space expansion"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "motifs in temporal networks"
    - "detecting strong ties using network motifs"
    - "distributed representation of subgraphs"
    - "graph2vec learning distributed representations of graphs"
    - "wide deep learning for recommender systems"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (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"

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)