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: "a tensor approach to learning mixed membership community models"
  Abstract: Community detection is the task of detecting hidden communities from observed interactions. Guaranteed community detection has so far been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we remove this restriction, and provide guaranteed community detection for a family of probabilistic network models with overlapping communities, termed as the mixed membership Dirichlet model, first introduced by Airoldi et al. This model allows for nodes to have fractional memberships in multiple communities and assumes that the community memberships are drawn from a Dirichlet distribution. Moreover, it contains the stochastic block model as a special case. We propose a unified approach to learning these models via a tensor spectral decomposition method. Our estimator is based on low-order moment tensor of the observed network, consisting of 3-star counts. Our learning method is fast and is based on simple linear algebraic operations, e.g. singular value decomposition and tensor power iterations. We provide guaranteed recovery of community memberships and model parameters and present a careful finite sample analysis of our learning method. As an important special case, our results match the best known scaling requirements for the (homogeneous) stochastic block model.

Connected through hyperedge HE-1 (induced by source paper "finding a latent k simplex in o k nnz data time via subset smoothing"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"
    - "improved spectral norm bounds for clustering"
    - "efficient learning of simplices"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-2 (induced by source paper "exit analysis for community detection"):
  with members:
    - "recovering a single community with side information"
    - "community detection with side information exact recovery under the stochastic block model"
    - "thinking fast and slow with deep learning and tree search"
    - "the power of side information in subgraph detection"
    - "exact recovery in the stochastic block model"
    - "comparative study for inference of hidden classes in stochastic block models"

Connected through hyperedge HE-3 (induced by source paper "when does non orthogonal tensor decomposition have no spurious local minima"):
  with members:
    - "on the optimization landscape of tensor decompositions"
    - "reinforcement learning of pomdp s using spectral methods"
    - "learning mixtures of gaussians in high dimensions"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "tensor decompositions for learning latent variable models"
    - "most tensor problems are np hard"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing via pairwise co occurrences identifiability and algorithms"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "learning hidden markov models from pairwise co occurrences with applications to topic modeling"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "spectral methods from tensor networks"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "estimation in the group action channel"
    - "bispectrum inversion with application to multireference alignment"
    - "heterogeneous multireference alignment a single pass approach"
    - "the sample complexity of multi reference alignment"
    - "exact tensor completion with sum of squares"
    - "sample complexity of the boolean multireference alignment problem"
    - "a nearly tight sum of squares lower bound for the planted clique problem"

Connected through hyperedge HE-6 (induced by source paper "prioritizing network communities"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "distributed representations of words and phrases and their compositionality"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"
    - "infinite multiple membership relational modeling for complex networks"

Connected through hyperedge HE-7 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition"):
  with members:
    - "detecting overlapping communities from local spectral subspaces"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "online tensor methods for learning latent variable models"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "community detection in networks with node attributes"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "community detection with side information exact recovery under the stochastic block model"):
  with members:
    - "the power of side information in subgraph detection"
    - "structure and inference in annotated networks"
    - "exact recovery in the stochastic block model"
    - "community detection via random and adaptive sampling"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in networks with node attributes"
    - "comparative study for inference of hidden classes in stochastic block models"

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)