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 efficient algorithms with hierarchical attentive memory"
  Abstract: In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM).  It is based on a binary tree with leaves corresponding to memory cells. This allows HAM to perform memory access in O(log n) complexity, which is a significant improvement over the standard attention mechanism that requires O(n) operations, where n is the size of the memory.  #N##N#We show that an LSTM network augmented with HAM can learn algorithms for problems like merging, sorting or binary searching from pure input-output examples.  In particular, it learns to sort n numbers in time O(n log n) and generalizes well to input sequences much longer than the ones seen during the training.  We also show that HAM can be trained to act like classic data structures: a stack, a FIFO queue and a priority queue.

Connected through hyperedge HE-1 (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 neural programs to parse programs"
    - "deepcoder learning to write programs"

Connected through hyperedge HE-2 (induced by source paper "progress extrapolating algorithmic learning to arbitrary sequence lengths"):
  with members:
    - "universal transformers"
    - "improving the neural gpu architecture for algorithm learning"
    - "making neural programming architectures generalize via recursion"
    - "adaptive computation time for recurrent neural networks"
    - "neural random access machines"
    - "neural programmer interpreters"
    - "neural gpus learn algorithms"
    - "neural turing machines"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "contextual memory trees"):
  with members:
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "prototypical networks for few shot learning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning to remember rare events"
    - "search engine guided non parametric neural machine translation"
    - "one shot learning with memory augmented neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "logarithmic time online multiclass prediction"

Connected through hyperedge HE-5 (induced by source paper "linear memory networks"):
  with members:
    - "self normalizing neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-6 (induced by source paper "improving the neural gpu architecture for algorithm learning"):
  with members:
    - "extensions and limitations of the neural gpu"
    - "recurrent dropout without memory loss"
    - "neural random access machines"
    - "learning simple algorithms from examples"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "learning to transduce with unbounded memory"
    - "grid long short term memory"
    - "neural programmer interpreters"

Connected through hyperedge HE-7 (induced by source paper "extensions and limitations of the neural gpu"):
  with members:
    - "adaptive computation time for recurrent neural networks"
    - "memory efficient backpropagation through time"
    - "end to end memory networks"
    - "neural random access machines"
    - "learning simple algorithms from examples"
    - "learning to transduce with unbounded memory"
    - "grid long short term memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"

Connected through hyperedge HE-8 (induced by source paper "learning explanatory rules from noisy data"):
  with members:
    - "adaptive neural compilation"
    - "programming with a differentiable forth interpreter"
    - "differentiable functional program interpreters"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "terpret a probabilistic programming language for program induction"
    - "neural programmer inducing latent programs with gradient descent"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "learning to transduce with unbounded memory"

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.LG(Machine Learning)