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: "breaking the small cluster barrier of graph clustering"
  Abstract: This paper investigates graph clustering in the planted cluster model in the presence of {\em small clusters}. Traditional results dictate that for an algorithm to provably correctly recover the clusters, {\em all} clusters must be sufficiently large (in particular, $\tilde{\Omega}(\sqrt{n})$ where $n$ is the number of nodes of the graph). We show that this is not really a restriction: by a more refined analysis of the trace-norm based recovery approach proposed in Jalali et al. (2011) and Chen et al. (2012), we prove that small clusters, under certain mild assumptions, do not hinder recovery of large ones. #R##N#Based on this result, we further devise an iterative algorithm to recover {\em almost all clusters} via a "peeling strategy", i.e., recover large clusters first, leading to a reduced problem, and repeat this procedure. These results are extended to the {\em partial observation} setting, in which only a (chosen) part of the graph is observed.The peeling strategy gives rise to an active learning algorithm, in which edges adjacent to smaller clusters are queried more often as large clusters are learned (and removed). #R##N#From a high level, this paper sheds novel insights on high-dimensional statistics and learning structured data, by presenting a structured matrix learning problem for which a one shot convex relaxation approach necessarily fails, but a carefully constructed sequence of convex relaxationsdoes the job.

Connected through hyperedge HE-1 (induced by source paper "searching for a single community in a graph"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "estimating the number of communities in a network"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "exact recovery in the stochastic block model"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "fast detection of overlapping communities via online tensor methods"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-2 (induced by source paper "graph sketching based space efficient data clustering"):
  with members:
    - "dynamic graph stream algorithms in o n space"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "a survey of signed network mining in social media"):
  with members:
    - "signed link analysis in social media networks"
    - "stochastic block model and exploratory analysis in signed networks"
    - "signed graph embedding when everybody can sit closer to friends than enemies"
    - "overlapping community detection in networks with positive and negative links"
    - "negative link prediction in social media"
    - "applications of structural balance in signed social networks"
    - "prediction and clustering in signed networks a local to global perspective"
    - "sitting closer to friends than enemies revisited"

Connected through hyperedge HE-4 (induced by source paper "a simple spectral algorithm for recovering planted partitions"):
  with members:
    - "nuclear norm minimization for the planted clique and biclique problems"

Connected through hyperedge HE-5 (induced by source paper "community detection via local dynamic interaction"):
  with members:
    - "defining and evaluating network communities based on ground truth"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-6 (induced by source paper "a tensor approach to learning mixed membership community models"):
  with members:
    - "dirichlet draws are sparse with high probability"
    - "fast detection of overlapping communities via online tensor methods"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "statistical algorithms and a lower bound for detecting planted clique"
    - "low distortion inference of latent similarities from a multiplex social network"
    - "finding overlapping communities in social networks toward a rigorous approach"

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.LG(Machine Learning)