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 "spiking neural predictive coding for continual learning from data streams"):
  Abstract: For energy-efficient computation in specialized neuromorphic hardware, we present the Spiking Neural Coding Network, an instantiation of a family of artificial neural models strongly motivated by the theory of predictive coding. The model, in essence, works by operating in a never-ending process of "guess-and-check", where neurons predict the activity values of one another and then immediately adjust their own activities to make better future predictions. The interactive, iterative nature of our neural system fits well into the continuous time formulation of data sensory stream prediction and, as we show, the model's structure yields a simple, local synaptic update rule, which could be used to complement or replace online spike-timing dependent plasticity rules. In this article, we experiment with an instantiation of our model that consists of leaky integrate-and-fire units. However, the general framework within which our model is situated can naturally incorporate more complex, formal neurons such as the Hodgkin-Huxley model. Our experimental results in pattern recognition demonstrate the potential of the proposed model when binary spike trains are the primary paradigm for inter-neuron communication. Notably, our model is competitive in terms of classification performance, capable of conducting online semi-supervised learning, and more computationally economical and biologically-plausible than popular artificial neural networks.

Includes members:
  Member "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule":
    also connected through other hyperedges:
      (none)
  Member "unsupervised learning by competing hidden units":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "biologically motivated deep learning method using hierarchical competitive learning")
  Member "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the neural state pushdown automata")
  Member "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploring the back alleys analysing the robustness of alternative neural network architectures against adversarial attacks")
  Member "like a baby visually situated neural language acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning as the unsupervised alignment of conceptual systems")
  Member "predicting cardiovascular risk factors in retinal fundus photographs using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")
  Member "continual learning of recurrent neural networks by locally aligning distributed representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)