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 "proximal algorithms and temporal differences for large linear systems extrapolation approximation and simulation"):
  Abstract: In this paper we consider large linear fixed point problems and solution with proximal algorithms. We show that, under certain assumptions, there is a close connection between proximal iterations, which are prominent in numerical analysis and optimization, and multistep methods of the temporal difference type such as TD(lambda), LSTD(lambda), and LSPE(lambda), which are central in simulation-based approximate dynamic programming. As an application of this connection, we show that we may accelerate the standard proximal algorithm by extrapolation towards the multistep iteration, which generically has a faster convergence rate. We also use the connection with multistep methods to integrate into the proximal algorithmic context several new ideas that have emerged in the approximate dynamic programming context. In particular, we consider algorithms that project each proximal iterate onto the subspace spanned by a small number of basis functions, using low-dimensional calculations and simulation, and we discuss various algorithmic options from approximate dynamic programming.

Includes members:
  Member "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "faster least squares approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "randomized spectral clustering in large scale stochastic block models")
  Member "relative error cur matrix decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")

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.NA(Numerical Analysis)