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: "bin ct urban waste collection based on predicting the container fill level"
  Abstract: Abstract   The fast demographic growth, together with the population concentration in cities and the increasing amount of daily waste, are factors that are pushing to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to optimally manage of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system called BIN-CT (BIN for the CiTy), based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data.  The objective of the system is to reduction the cost of the waste collection service minimizing the distance traveled by a truck to collect the waste from a container, thereby reducing the fuel consumption. At the same time the quality of service for the citizen is increased, avoiding the annoying overflows of containers thanks to the accurate fill-level predictions given by BIN-CT. In this article we show the features of our software system, illustrating its operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary trips to containers, reduces the distance traveled to collect a container by 20%, and generates routes 33.2% shorter than the routes used by the company. Therefore it enables a considerable reduction of total costs and harmful emissions thrown up into the atmosphere.

Connected through hyperedge HE-1 (induced by source paper "bayesian neural architecture search using a training free performance metric"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "evolutionary architecture search for deep multitask networks"
    - "metaheuristic design of feedforward neural networks"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"):
  with members:
    - "low cost recurrent neural network expected performance evaluation"
    - "evolutionary architecture search for deep multitask networks"
    - "dlopt deep learning optimization library"
    - "evolving deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "metaheuristic design of feedforward neural networks"
    - "metaheuristic algorithms for convolution neural network"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

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)