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: "segen sample ensemble genetic evolutionary network model"
  Abstract: Deep learning, a rebranding of deep neural network research works, has achieved remarkable success in recent years. With multiple hidden layers, deep learning models aim at computing hierarchical features or representations of the observational data. Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning efforts and the lack of result explainability, deep learning has also suffered from lots of criticism. In this paper, we will introduce a new representation learning model, namely "Sample-Ensemble Genetic Evolutional Network" (SEGEN), which can serve as an alternative approach to deep learning models. Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutional learning strategy to build a group of unit models generations by generations. The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much "smaller" and "shallower" architecture. The learning results of each instance at the final generation will be effectively combined from each unit model via diffusive propagation and ensemble learning strategies. From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning works, but has sound theoretic interpretability of the learning process and results. Extensive experiments have been done on real-world network structured datasets, and the experimental results obtained by SEGEN have demonstrate its advantages over the other state-of-the-art representation learning models.

Connected through hyperedge HE-1 (induced by source paper "graph bert only attention is needed for learning graph representations"):
  with members:
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "transformer xl attentive language models beyond a fixed length context"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "rethinking imagenet pre training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep loopy neural network model for graph structured data representation learning"
    - "junction tree variational autoencoder for molecular graph generation"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "gradient descent based optimization algorithms for deep learning models training"):
  with members:
    - "gadam genetic evolutionary adam for deep neural network optimization"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "adam a method for stochastic optimization"
    - "genetic algorithms for evolving deep neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "graph neural networks for small graph and giant network representation learning an overview"):
  with members:
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "graph neural lasso for dynamic network regression"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "gadam genetic evolutionary adam for deep neural network optimization"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "improving generalization performance by switching from adam to sgd"
    - "adam a method for stochastic optimization"
    - "genetic algorithms for evolving deep neural networks"

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)