human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the ant swarm neuro evolution procedure for optimizing recurrent networks"):
  Abstract: Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, we propose a novel neuro-evolution algorithm based on ant colony optimization (ACO), called ant swarm neuro-evolution (ASNE), for directly optimizing RNN topologies. The procedure selects from multiple modern recurrent cell types such as Delta-RNN, GRU, LSTM, MGU and UGRNN cells, as well as recurrent connections which may span multiple layers and/or steps of time. In order to introduce an inductive bias that encourages the formation of sparser synaptic connectivity patterns, we investigate several variations of the core algorithm. We do so primarily by formulating different functions that drive the underlying pheromone simulation process (which mimic L1 and L2 regularization in standard machine learning) as well as by introducing ant agents with specialized roles (inspired by how real ant colonies operate), i.e., explorer ants that construct the initial feed forward structure and social ants which select nodes from the feed forward connections to subsequently craft recurrent memory structures. We also incorporate a Lamarckian strategy for weight initialization which reduces the number of backpropagation epochs required to locally train candidate RNNs, speeding up the neuro-evolution process. Our results demonstrate that the sparser RNNs evolved by ASNE significantly outperform traditional one and two layer architectures consisting of modern memory cells, as well as the well-known NEAT algorithm. Furthermore, we improve upon prior state-of-the-art results on the time series dataset utilized in our experiments.

Includes members:
  Member "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian neural architecture search using a training free performance metric")
  Member "investigating recurrent neural network memory structures using neuro evolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian neural architecture search using a training free performance metric")
  Member "low cost recurrent neural network expected performance evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian neural architecture search using a training free performance metric")
  Member "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "investigating recurrent neural network memory structures using neuro evolution")
  Member "evolving deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "learning simpler language models with the differential state framework":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spiking neural predictive coding for continual learning from data streams")
  Member "large scale evolution of convolutional neural networks using volunteer computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "investigating recurrent neural network memory structures using neuro evolution")
  Member "minimal gated unit for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")

Question: Please tell me which class the source paper 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)