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: "random tensors and planted cliques"
  Abstract: The r-parity tensor of a graph is a generalization of the adjacency matrix, where the tensor's entries denote the parity of the number of edges in subgraphs induced by r distinct vertices. For r=2, it is the adjacency matrix with 1's for edges and -1's for nonedges. It is well-known that the 2-norm of the adjacency matrix of a random graph is O(\sqrt{n}). Here we show that the 2-norm of the r-parity tensor is at most f(r)\sqrt{n}\log^{O(r)}n, answering a question of Frieze and Kannan who proved this for r=3. As a consequence, we get a tight connection between the planted clique problem and the problem of finding a vector that approximates the 2-norm of the r-parity tensor of a random graph. Our proof method is based on an inductive application of concentration of measure.

Connected through hyperedge HE-1 (induced by source paper "optimal average case reductions to sparse pca from weak assumptions to strong hardness"):
  with members:
    - "on finding dense common subgraphs"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "the power of sum of squares for detecting hidden structures"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "incoherence optimal matrix completion"
    - "label optimal regret bounds for online local learning"

Connected through hyperedge HE-2 (induced by source paper "strongly refuting random csps below the spectral threshold"):
  with members:
    - "certifying random polynomials over the unit sphere via sum of squares hierarchy"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor principal component analysis via sum of squares proofs"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-3 (induced by source paper "weak decoupling polynomial folds and approximate optimization over the sphere"):
  with members:
    - "the power of sum of squares for detecting hidden structures"
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "sum of squares certificates for maxima of random tensors on the sphere"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "certifying random polynomials over the unit sphere via sum of squares hierarchy"
    - "strongly refuting random csps below the spectral threshold"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"

Connected through hyperedge HE-4 (induced by source paper "spectral properties of hypergraph laplacian and approximation algorithms"):
  with members:
    - "spectral properties of laplacian and stochastic diffusion process for edge expansion in hypergraphs"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "the complexity of approximating vertex expansion"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "reductions between expansion problems"

Connected through hyperedge HE-5 (induced by source paper "sum of squares certificates for maxima of random tensors on the sphere"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "strongly refuting random csps below the spectral threshold"
    - "tensor principal component analysis via sum of squares proofs"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "a statistical model for tensor pca"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "hypercontractivity sum of squares proofs and their applications"

Connected through hyperedge HE-6 (induced by source paper "hypergraph markov operators eigenvalues and approximation algorithms"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "the complexity of approximating vertex expansion"
    - "approximation algorithm for sparsest k partitioning"
    - "many sparse cuts via higher eigenvalues"
    - "min max graph partitioning and small set expansion"
    - "most tensor problems are np hard"

Connected through hyperedge HE-7 (induced by source paper "fourier pca and robust tensor decomposition"):
  with members:
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "blind signal separation in the presence of gaussian noise"
    - "tensor decompositions for learning latent variable models"
    - "efficient learning of simplices"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"

Connected through hyperedge HE-8 (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"
    - "breaking the small cluster barrier of graph clustering"
    - "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"

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)