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 "weighted tensor completion for time series causal inference"):
  Abstract: Marginal Structural Models (MSM) {Robins, 2000} are the most popular models for causal inference from time-series observational data. However, they have two main drawbacks: (a) they do not capture subject heterogeneity, and (b) they only consider fixed time intervals and do not scale gracefully with longer intervals. In this work, we propose a new family of MSMs to address these two concerns. We model the potential outcomes as a three-dimensional tensor of low rank, where the three dimensions correspond to the agents, time periods and the set of possible histories. Unlike the traditional MSM, we allow the dimensions of the tensor to increase with the number of agents and time periods. We set up a weighted tensor completion problem as our estimation procedure, and show that the solution to this problem converges to the true model in an appropriate sense. Then we show how to solve the estimation problem, providing conditions under which we can approximately and efficiently solve the estimation problem. Finally, we propose an algorithm based on projected gradient descent, which is easy to implement and evaluate its performance on a simulated dataset.

Includes members:
  Member "relative error tensor low rank approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "tensorly tensor learning in python":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "applying tensor decomposition to image for robustness against adversarial attack")
  Member "spectral algorithms for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "noisy tensor completion via the sum of squares hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "identification of conditional interventional distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "separators and adjustment sets in causal graphs complete criteria and an algorithmic framework")
  Member "learning mixtures of spherical gaussians moment methods and spectral decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "a spectral algorithm for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when can wasserstein gans minimize wasserstein distance")

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)