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: "tensor principal component analysis via sum of squares proofs"
  Abstract: We study a statistical model for the tensor principal component analysis problem introduced by Montanari and Richard: Given a order-$3$ tensor $T$ of the form $T = \tau \cdot v_0^{\otimes 3} + A$, where $\tau \geq 0$ is a signal-to-noise ratio, $v_0$ is a unit vector, and $A$ is a random noise tensor, the goal is to recover the planted vector $v_0$. For the case that $A$ has iid standard Gaussian entries, we give an efficient algorithm to recover $v_0$ whenever $\tau \geq \omega(n^{3/4} \log(n)^{1/4})$, and certify that the recovered vector is close to a maximum likelihood estimator, all with high probability over the random choice of $A$. The previous best algorithms with provable guarantees required $\tau \geq \Omega(n)$. #R##N#In the regime $\tau \leq o(n)$, natural tensor-unfolding-based spectral relaxations for the underlying optimization problem break down (in the sense that their integrality gap is large). To go beyond this barrier, we use convex relaxations based on the sum-of-squares method. Our recovery algorithm proceeds by rounding a degree-$4$ sum-of-squares relaxations of the maximum-likelihood-estimation problem for the statistical model. To complement our algorithmic results, we show that degree-$4$ sum-of-squares relaxations break down for $\tau \leq O(n^{3/4}/\log(n)^{1/4})$, which demonstrates that improving our current guarantees (by more than logarithmic factors) would require new techniques or might even be intractable. #R##N#Finally, we show how to exploit additional problem structure in order to solve our sum-of-squares relaxations, up to some approximation, very efficiently. Our fastest algorithm runs in nearly-linear time using shifted (matrix) power iteration and has similar guarantees as above. The analysis of this algorithm also confirms a variant of a conjecture of Montanari and Richard about singular vectors of tensor unfoldings.

Connected through hyperedge HE-1 (induced by source paper "computational hardness of certifying bounds on constrained pca problems"):
  with members:
    - "mean field approximation convex hierarchies and the optimality of correlation rounding a unified perspective"
    - "heterogeneous multireference alignment a single pass approach"
    - "community detection in hypergraphs spiked tensor models and sum of squares"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"

Connected through hyperedge HE-2 (induced by source paper "algorithms for an efficient tensor biclustering"):
  with members:
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-3 (induced by source paper "on the connection between learning two layers neural networks and tensor decomposition"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the power of sum of squares for detecting hidden structures"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "polynomial time tensor decompositions with sum of squares"

Connected through hyperedge HE-4 (induced by source paper "polynomial time probabilistic reasoning with partial observations via implicit learning in probability logics"):
  with members:
    - "bayesian estimation from few samples community detection and related problems"
    - "provable learning of noisy or networks"
    - "rounding sum of squares relaxations"
    - "hypercontractivity sum of squares proofs and their applications"
    - "from statistical knowledge bases to degrees of belief"
    - "characterizing and reasoning about probabilistic and non probabilistic expectation"

Connected through hyperedge HE-5 (induced by source paper "mixture models robustness and sum of squares proofs"):
  with members:
    - "thresholding based efficient outlier robust pca"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-6 (induced by source paper "community detection in hypergraphs spiked tensor models and sum of squares"):
  with members:
    - "community recovery in hypergraphs"
    - "certifying random polynomials over the unit sphere via sum of squares hierarchy"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "a statistical model for tensor pca"
    - "exact recovery in the stochastic block model"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-7 (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 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"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-8 (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"
    - "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"
    - "random tensors and planted cliques"

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)