human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"):
  Abstract: The popular Alternating Least Squares (ALS) algorithm for tensor decomposition is extremely efficient, but often converges to poor local optima, particularly when the weights of the factors are non-uniform. We propose a modification of the ALS approach that is as efficient as standard ALS, but provably recovers the true factors with random initialization under standard incoherence assumptions on the factors of the tensor. We demonstrate the significant practical superiority of our approach over traditional ALS (with both random initialization and SVD-based initialization) for a variety of tasks on synthetic data - including tensor factorization on exact, noisy and over-complete tensors, as well as tensor completion - and for computing word embeddings from a third-order word tri-occurrence tensor.

Includes members:
  Member "a practical randomized cp tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "learning from multiway data simple and efficient tensor regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "polynomial time tensor decompositions with sum of squares":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficiently learning structured distributions from untrusted batches")
  Member "reinforcement learning of pomdps using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "tensorly tensor learning in python":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "applying tensor decomposition to image for robustness against adversarial attack")
  Member "tensor factorization via matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "embedding syntax and semantics of prepositions via tensor decomposition")
  Member "learning mixtures of gaussians in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "decomposing overcomplete 3rd order tensors using sum of squares algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning two layer rectified neural networks in polynomial time")

Question: Please tell me which class the source paper 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)