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: "densely connected bidirectional lstm with applications to sentence classification"
  Abstract: Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space. However, since most deep architectures like stacked RNNs tend to suffer from the vanishing-gradient and overfitting problems, their effects are still understudied in many NLP tasks. Inspired by this, we propose a novel multi-layer RNN model called densely connected bidirectional long short-term memory (DC-Bi-LSTM) in this paper, which essentially represents each layer by the concatenation of its hidden state and all preceding layers' hidden states, followed by recursively passing each layer's representation to all subsequent layers. We evaluate our proposed model on five benchmark datasets of sentence classification. DC-Bi-LSTM with depth up to 20 can be successfully trained and obtain significant improvements over the traditional Bi-LSTM with the same or even less parameters. Moreover, our model has promising performance compared with the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "automatic crack detection and classification by exploiting statistical event descriptors for deep learning"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-2 (induced by source paper "recurrently controlled recurrent networks"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "learning to generate reviews and discovering sentiment"
    - "sentence state lstm for text representation"
    - "simple recurrent units for highly parallelizable recurrence"

Connected through hyperedge HE-3 (induced by source paper "densely connected attention propagation for reading comprehension"):
  with members:
    - "a question focused multi factor attention network for question answering"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "attention guided answer distillation for machine reading comprehension"
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"

Connected through hyperedge HE-4 (induced by source paper "phramacovigilance exploring deep learning techniques for identifying mentions of medication intake from twitter"):
  with members:
    - "do convolutional networks need to be deep for text classification"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "recurrent neural network for text classification with multi task learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "going deeper with convolutions"
    - "convolutional neural networks for sentence classification"

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.CL(Computation and Language)