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: "gated feedback recurrent neural networks"
  Abstract: In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recurrent layers by allowing and controlling signals flowing from upper recurrent layers to lower layers using a global gating unit for each pair of layers. The recurrent signals exchanged between layers are gated adaptively based on the previous hidden states and the current input. We evaluated the proposed GF-RNN with different types of recurrent units, such as tanh, long short-term memory and gated recurrent units, on the tasks of character-level language modeling and Python program evaluation. Our empirical evaluation of different RNN units, revealed that in both tasks, the GF-RNN outperforms the conventional approaches to build deep stacked RNNs. We suggest that the improvement arises because the GF-RNN can adaptively assign different layers to different timescales and layer-to-layer interactions (including the top-down ones which are not usually present in a stacked RNN) by learning to gate these interactions.

Connected through hyperedge HE-1 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "augmenting self attention with persistent memory"
    - "compressive transformers for long range sequence modelling"
    - "pre trained language model representations for language generation"
    - "r transformer recurrent neural network enhanced transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"

Connected through hyperedge HE-2 (induced by source paper "memo a deep network for flexible combination of episodic memories"):
  with members:
    - "relational deep reinforcement learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive neural networks for efficient inference"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "feedback network for image super resolution"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "hamiltonian neural networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised and interpretable domain adaptation to rapidly filter social web data for emergency services"):
  with members:
    - "diversity based generalization for neural unsupervised text classification under domain shift"
    - "domain adaptation with adversarial training and graph embeddings"
    - "advances in pre training distributed word representations"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "effective approaches to attention based neural machine translation"
    - "learning multiple tasks with deep relationship networks"
    - "learning multiple tasks with multilinear relationship networks"

Connected through hyperedge HE-5 (induced by source paper "adaptive guidance and integrated navigation with reinforcement meta learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "deep reinforcement learning for six degree of freedom planetary powered descent and landing"
    - "meta learning shared hierarchies"
    - "a simple neural attentive meta learner"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"

Connected through hyperedge HE-6 (induced by source paper "po emo conceptualization annotation and modeling of aesthetic emotions in german and english poetry"):
  with members:
    - "towards multimodal emotion recognition in german speech events in cars using transfer learning"
    - "dens a dataset for multi class emotion analysis"
    - "semantic change and emerging tropes in a large corpus of new high german poetry"
    - "diachronic topics in new high german poetry"
    - "multi task learning for argumentation mining in low resource settings"
    - "iest wassa 2018 implicit emotions shared task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-7 (induced by source paper "pelican a deep residual network for network intrusion detection"):
  with members:
    - "a transfer learning approach for network intrusion detection"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "convolutional neural networks at constrained time cost"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

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)