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: "inferring algorithmic patterns with stack augmented recurrent nets"
  Abstract: Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.

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 "modeling programs hierarchically with stack augmented lstm"):
  with members:
    - "maybe deep neural networks are the best choice for modeling source code"
    - "open vocabulary learning on source code with a graph structured cache"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "code completion with neural attention and pointer networks"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "attention is all you need"
    - "learning to represent programs with graphs"

Connected through hyperedge HE-3 (induced by source paper "encoding based memory modules for recurrent neural networks"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "linear memory networks"
    - "lstm a search space odyssey"
    - "recent trends in deep learning based natural language processing"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "recurrent highway networks"

Connected through hyperedge HE-4 (induced by source paper "a data efficient end to end spoken language understanding architecture"):
  with members:
    - "hybrid neural models for sequence modelling the best of three worlds"
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "towards end to end spoken language understanding"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "asr error management for improving spoken language understanding"
    - "listen and translate a proof of concept for end to end speech to text translation"

Connected through hyperedge HE-5 (induced by source paper "grammar filtering for syntax guided synthesis"):
  with members:
    - "learning to infer program sketches"
    - "synthetic datasets for neural program synthesis"
    - "neural guided deductive search for real time program synthesis from examples"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "sygus comp 2017 results and analysis"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "learning neural programs to parse programs"

Connected through hyperedge HE-6 (induced by source paper "ordered memory"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "cooperative learning of disjoint syntax and semantics"
    - "listops a diagnostic dataset for latent tree learning"
    - "improved sentence modeling using suffix bidirectional lstm"
    - "systematic generalization what is required and can it be learned"
    - "deep contextualized word representations"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-7 (induced by source paper "large memory layers with product keys"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "cross lingual language model pretraining"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-8 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

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)