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 "autoencoding with xcsf"):
  Abstract: Autoencoders enable data dimensionality reduction and are a key component of many (deep) learning systems. This article explores the use of the XCSF online evolutionary reinforcement learning system to perform autoencoding. Initial results using a neural network representation and combining artificial evolution with stochastic gradient descent, suggest it is an effective approach to data reduction. The approach adaptively subdivides the input domain into local approximations that are simpler than a global neural network solution. By allowing the number of neurons in the autoencoders to evolve, this further enables the emergence of an ensemble of structurally heterogeneous solutions to cover the problem space. In this case, networks of differing complexity are typically seen to cover different areas of the problem space. Furthermore, the rate of gradient descent applied to each layer is tuned via self-adaptive mutation, thereby reducing the parameter optimisation task.

Includes members:
  Member "evolving unsupervised deep neural networks for learning meaningful representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi objective genetic programming for manifold learning balancing quality and dimensionality")
  Member "weight agnostic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  Member "autoencoding with a learning classifier system initial results":
    also connected through other hyperedges:
      (none)
  Member "evolutionary stochastic gradient descent for optimization of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "acoustic model optimization based on evolutionary stochastic gradient descent with anchors for automatic speech recognition")
  Member "population based training of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "convolution by evolution differentiable pattern producing networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding online neural update rules by learning to remember")
  Member "a cognitive architecture based on a learning classifier system with spiking classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evolving spiking neural networks for nonlinear control problems")
  Member "delving deep into rectifiers surpassing human level performance on imagenet classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)