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: "learning dynamic boltzmann machines with spike timing dependent plasticity"
  Abstract: We propose a particularly structured Boltzmann machine, which we refer to as a dynamic Boltzmann machine (DyBM), as a stochastic model of a multi-dimensional time-series. The DyBM can have infinitely many layers of units but allows exact and efficient inference and learning when its parameters have a proposed structure. This proposed structure is motivated by postulates and observations, from biological neural networks, that the synaptic weight is strengthened or weakened, depending on the timing of spikes (i.e., spike-timing dependent plasticity or STDP). We show that the learning rule of updating the parameters of the DyBM in the direction of maximizing the likelihood of given time-series can be interpreted as STDP with long term potentiation and long term depression. The learning rule has a guarantee of convergence and can be performed in a distributed matter (i.e., local in space) with limited memory (i.e., local in time).

Connected through hyperedge HE-1 (induced by source paper "time discounting convolution for event sequences with ambiguous timestamps"):
  with members:
    - "modeling the intensity function of point process via recurrent neural networks"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "unsupervised representation learning of structured radio communication signals"
    - "recurrent neural networks for multivariate time series with missing values"
    - "conditional random fields as recurrent neural networks"
    - "neural machine translation of rare words with subword units"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning to update auto associative memory in recurrent neural networks for improving sequence memorization"):
  with members:
    - "end to end answer chunk extraction and ranking for reading comprehension"
    - "deep reinforcement learning for dialogue generation"
    - "squad 100 000 questions for machine comprehension of text"
    - "recurrent highway networks"
    - "dense associative memory for pattern recognition"
    - "neural architecture search with reinforcement learning"
    - "sequence to sequence rnns for text summarization"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-3 (induced by source paper "boltzmann machines for time series"):
  with members:
    - "boltzmann machines and energy based models"
    - "regularized dynamic boltzmann machine with delay pruning for unsupervised learning of temporal sequences"
    - "learning binary or real valued time series via spike timing dependent plasticity"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "an objective function for stdp"
    - "adam a method for stochastic optimization"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-4 (induced by source paper "a framework for searching for general artificial intelligence"):
  with members:
    - "progressive neural networks"
    - "convolutional neural fabrics"
    - "online adaptation of deep architectures with reinforcement learning"
    - "learning purposeful behaviour in the absence of rewards"
    - "feedforward initialization for fast inference of deep generative networks is biologically plausible"
    - "deep vs shallow networks an approximation theory perspective"
    - "programming with a differentiable forth interpreter"
    - "why artificial intelligence needs a task theory and what it might look like"

Connected through hyperedge HE-5 (induced by source paper "learning binary or real valued time series via spike timing dependent plasticity"):
  with members:
    - "regularized dynamic boltzmann machine with delay pruning for unsupervised learning of temporal sequences"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "an objective function for stdp"

Connected through hyperedge HE-6 (induced by source paper "regularized dynamic boltzmann machine with delay pruning for unsupervised learning of temporal sequences"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "adam a method for stochastic optimization"

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)