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: "adaptive closed loop ofdm based resource allocation method using machine learning and genetic algorithm"
  Abstract: in this paper, the concept of Machine Learning (ML) is introduced to the Orthogonal Frequency Division Multiple Access-based (OFDMA-based) scheduler. Similar to the impact of the Channel Quality Indicator (CQI) on the scheduler in the Long Term Evolution (LTE), ML is utilized to provide the scheduler with pertinent information about the User Equipment (UE) traffic patterns, demands, Quality of Service (QoS) requirements, instantaneous user throughput and other network conditions. An adaptive ML-based framework is proposed in order to optimize the LTE scheduler operation. The proposed technique targets multiple objective scheduling strategies. The weights of the different objectives are adjusted to optimize the resources allocation per transmission based on the UEs demand pattern. In addition, it overcomes the trade-off problem of the traditional scheduling methods. The technique can be used as a generic framework with any scheduling strategy. In this paper, Genetic Algorithm-based (GA-based) multiobjective scheduler is considered to illustrate the efficiency of the proposed adaptive scheduling solution. Results show that using the combination of clustering and classification algorithms along with the GA optimizes the GA scheduler functionality and makes use of the ML process to form a closed loop scheduling mechanism. Keywords—Scheduling; OFDMA; LTE; Machine Learning; Clustering; Classification; Genetic Algorithm;

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.NI(Networking and Internet Architecture)