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: "catching the head tail and everything in between a streaming algorithm for the degree distribution"
  Abstract: The degree distribution is one of the most fundamental graph properties of interest for real-world graphs. It has been widely observed in numerous domains that graphs typically have a tailed or scale-free degree distribution. While the average degree is usually quite small, the variance is quite high and there are vertices with degrees at all scales. We focus on the problem of approximating the degree distribution of a large streaming graph, with small storage. We design an algorithm headtail, whose main novelty is a new estimator of infrequent degrees using truncated geometric random variables. We give a mathematical analysis of headtail and show that it has excellent behavior in practice. We can process streams will millions of edges with storage less than 1% and get extremely accurate approximations for all scales in the degree distribution. #R##N#We also introduce a new notion of Relative Hausdorff distance between tailed histograms. Existing notions of distances between distributions are not suitable, since they ignore infrequent degrees in the tail. The Relative Hausdorff distance measures deviations at all scales, and is a more suitable distance for comparing degree distributions. By tracking this new measure, we are able to give strong empirical evidence of the convergence of headtail.

Connected through hyperedge HE-1 (induced by source paper "temporal network sampling"):
  with members:
    - "from community to role based graph embeddings"
    - "temporal network representation learning"
    - "dynamic joint variational graph autoencoders"
    - "influential user subscription on time decaying social streams"
    - "explanations for temporal recommendations"
    - "sampling for approximate bipartite network projection"
    - "on sampling from massive graph streams"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "relative hausdorff distance for network analysis"):
  with members:
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"
    - "computing the gromov hausdorff distance for metric trees"
    - "graph based anomaly detection and description a survey"
    - "quantification and comparison of degree distributions in complex networks"
    - "information spreading in dynamic graphs"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (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"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "approximately counting triangles in sublinear time"
    - "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-4 (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"
    - "why do simple algorithms for triangle enumeration work in the real world"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
    - "listing all maximal cliques in sparse graphs in near optimal 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)