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: "variations on memetic algorithms for graph coloring problems"
  Abstract: Graph vertices coloring with a given number of colors is a famous and much-studied NP-complete problem. The best methods to solve this problem are hybrid algorithms such as memetic algorithms [Galinier99, Lu10, Wu12] or quantum annealing [Titiloye11a, Titiloye11b, Titiloye12]. Those hybrid algorithms use a powerful local search inside a population-based algorithm. The balance between intensification and diversification is essential for those metaheuristics but difficult to archieve. Customizing metaheuristics takes long time and is one of the main weak points of these approaches. This paper studies the impact of the increase and the decrease of diversification in one of the most effective algorithms known: the Hybrid Evolutionary Algorithm (HEA) from Galinier and Hao [Galinier99]. We then propose a modification of this memetic algorithm in order to work with a population of only two individuals. This new algorithm more effectively manages the correct 'dose' of diversification to add into the included local search - TabuCol [Hertz87] in the case of the HEA. It has produced several good results for the well-known DIMACS benchmark graphs, such as 47-colorings for DSJC500.5, 82-colorings for DSJC1000.5, 222-colorings for DSJC1000.9 and 81-colorings for flat1000\_76\_0, which have so far only been produced by quantum annealing [Titiloye12] in 2012 with massive multi-CPUs.

Connected through hyperedge HE-1 (induced by source paper "gradient descent based weight learning for grouping problems application on graph coloring and equitable graph coloring"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "a dsatur based algorithm for the equitable coloring problem"
    - "adam a method for stochastic optimization"
    - "a tabu search heuristic for the equitable coloring problem"
    - "recent advances in graph partitioning"
    - "deep generative stochastic networks trainable by backprop"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "breaking instance independent symmetries in exact graph coloring"

Connected through hyperedge HE-2 (induced by source paper "optimality clue for graph coloring problem"):
  with members:
    - "uniform solution sampling using a constraint solver as an oracle"

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.AI(Artificial Intelligence)