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: "transfer learning based dynamic multiobjective optimization algorithms"
  Abstract: One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is that optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing “experiences” to construct a prediction model via statistical machine learning approaches. However, most existing methods neglect the nonindependent and identically distributed nature of data to construct the prediction model. In this paper, we propose an algorithmic framework, called transfer learning-based dynamic multiobjective evolutionary algorithm (EA), which integrates transfer learning and population-based EAs to solve the DMOPs. This approach exploits the transfer learning technique as a tool to generate an effective initial population pool via reusing past experience to speed up the evolutionary process, and at the same time any population-based multiobjective algorithms can benefit from this integration without any extensive modifications. To verify this idea, we incorporate the proposed approach into the development of three well-known EAs, nondominated sorting genetic algorithm II, multiobjective particle swarm optimization, and the regularity model-based multiobjective estimation of distribution algorithm. We employ 12 benchmark functions to test these algorithms as well as compare them with some chosen state-of-the-art designs. The experimental results confirm the effectiveness of the proposed design for DMOPs.

Connected through hyperedge HE-1 (induced by source paper "neural networks in evolutionary dynamic constrained optimization computational cost and benefits"):
  with members:
    - "a comparison of constraint handling techniques for dynamic constrained optimization problems"

Connected through hyperedge HE-2 (induced by source paper "solving dynamic multi objective optimization problems using incremental support vector machine"):
  with members:
    - "solving dynamic multi objective optimization problems via support vector machine"

Connected through hyperedge HE-3 (induced by source paper "evolutionary dynamic multi objective optimization via regression transfer learning"):
  with members:
    - "solving dynamic multi objective optimization problems using incremental support vector machine"

Connected through hyperedge HE-4 (induced by source paper "automatically designing cnn architectures using genetic algorithm for image classification"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "igd indicator based evolutionary algorithm for many objective optimization problems"
    - "improved regularity model based eda for many objective optimization"
    - "large scale evolution of image classifiers"
    - "skip connections eliminate singularities"
    - "efficient architecture search by network transformation"
    - "hierarchical representations for efficient architecture search"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-5 (induced by source paper "solving dynamic multi objective optimization problems via support vector machine"):
  with members:
    - "populations can be essential in tracking dynamic optima"

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.NE(Neural and Evolutionary Computing)