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: "memory visualization for gated recurrent neural networks in speech recognition"
  Abstract: Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties behind the remarkable performance remain unclear in many applications, e.g., automatic speech recognition (ASR). This paper employs visualization techniques to study the behavior of LSTM and GRU when performing speech recognition tasks. Our experiments show some interesting patterns in the gated memory, and some of them have inspired simple yet effective modifications on the network structure. We report two of such modifications: (1) lazy cell update in LSTM, and (2) shortcut connections for residual learning. Both modifications lead to more comprehensible and powerful networks.

Connected through hyperedge HE-1 (induced by source paper "discovery of natural language concepts in individual units of cnns"):
  with members:
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "constituency parsing with a self attentive encoder"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "network dissection quantifying interpretability of deep visual representations"
    - "attention is all you need"
    - "interpreting deep visual representations via network dissection"
    - "what do neural machine translation models learn about morphology"

Connected through hyperedge HE-2 (induced by source paper "the emergence of number and syntax units in lstm language models"):
  with members:
    - "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"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "colorless green recurrent networks dream hierarchically"
    - "learning to generate reviews and discovering sentiment"
    - "targeted syntactic evaluation of language models"

Connected through hyperedge HE-3 (induced by source paper "an optimized and energy efficient parallel implementation of non iteratively trained recurrent neural networks"):
  with members:
    - "scaling back propagation by parallel scan algorithm"
    - "accelerating recurrent neural network training using sequence bucketing and multi gpu data parallelization"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep learning in neural networks"
    - "attention based models for speech recognition"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "testrnn coverage guided testing on recurrent neural networks"):
  with members:
    - "test metrics for recurrent neural networks"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "concolic testing for deep neural networks"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "understanding hidden memories of recurrent neural networks"):
  with members:
    - "lstm a search space odyssey"
    - "automatic rule extraction from long short term memory networks"
    - "towards better analysis of deep convolutional neural networks"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing and understanding neural models in nlp"
    - "inverting visual representations with convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"):
  with members:
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "a fast unified model for parsing and sentence understanding"
    - "neural semantic role labeling with dependency path embeddings"
    - "theano a python framework for fast computation of mathematical expressions"
    - "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"
    - "visualizing and understanding neural models in nlp"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-7 (induced by source paper "representations of language in a model of visually grounded speech signal"):
  with members:
    - "learning word like units from joint audio visual analysis"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "recurrent highway networks"
    - "understanding neural networks through representation erasure"
    - "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"
    - "pixel recurrent neural networks"
    - "deep residual learning for image recognition"

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)