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 "gradient descent based weight learning for grouping problems application on graph coloring and equitable graph coloring"):
  Abstract: A grouping problem involves partitioning a set of items into mutually disjoint groups or clusters according to some guiding decision criteria and imperative constraints. Grouping problems have many relevant applications and are computationally difficult. In this work, we present a general weight learning based optimization framework for solving grouping problems. The central idea of our approach is to formulate the task of seeking a solution as a real-valued weight matrix learning problem that is solved by first order gradient descent. A practical implementation of this framework is proposed with tensor calculus in order to benefit from parallel computing on GPU devices. To show its potential for tackling difficult problems, we apply the approach to two typical and well-known grouping problems (graph coloring and equitable graph coloring). We present large computational experiments and comparisons on popular benchmarks and report improved best-known results (new upper bounds) for several large graphs.

Includes members:
  Member "an overview of gradient descent optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "a dsatur based algorithm for the equitable coloring problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectrum graph coloring and applications to wi fi channel assignment")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "variations on memetic algorithms for graph coloring problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimality clue for graph coloring problem")
  Member "a tabu search heuristic for the equitable coloring problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an exact approach for the balanced k way partitioning problem with weight constraints and its application to sports team realignment")
  Member "recent advances in graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "deep generative stochastic networks trainable by backprop":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders")
  Member "estimating or propagating gradients through stochastic neurons for conditional computation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)