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: "context free transductions with neural stacks"
  Abstract: This paper analyzes the behavior of stack-augmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, context-free language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.

Connected through hyperedge HE-1 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"

Connected through hyperedge HE-2 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"
    - "dilated recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "memory augmented recurrent neural networks can learn generalized dyck languages"):
  with members:
    - "theoretical limitations of self attention in neural sequence models"
    - "sequential neural networks as automata"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "attention is all you need"
    - "lie access neural turing machine"
    - "learning operations on a stack with neural turing machines"
    - "neural random access machines"

Connected through hyperedge HE-4 (induced by source paper "the neural state pushdown automata"):
  with members:
    - "on the computational power of rnns"
    - "learning by abstraction the neural state machine"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "number sequence prediction problems for evaluating computational powers of neural networks"
    - "biologically motivated algorithms for propagating local target representations"
    - "evaluating the ability of lstms to learn context free grammars"
    - "unbiased online recurrent optimization"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-5 (induced by source paper "lstm networks can perform dynamic counting"):
  with members:
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "evaluating the ability of lstms to learn context free grammars"
    - "learning operations on a stack with neural turing machines"
    - "learning to transduce with unbounded memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "sequential neural networks as automata"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "rational recurrences"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "attention is all you need"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "effective approaches to attention based neural machine translation"
    - "character aware neural language models"
    - "attention based models for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "finding syntactic representations in neural stacks"):
  with members:
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "targeted syntactic evaluation of language models"
    - "grammar induction with neural language models an unusual replication"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "learning to transduce with unbounded memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "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)