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: "denoising bodies to titles retrieving similar questions with recurrent convolutional models"
  Abstract: Question answering forums are rapidly growing in size with no automated ability to refer to and reuse existing answers. In this paper, we develop a methodology for finding semantically related questions. The task is difficult since 1) key pieces of information are often buried in extraneous detail in the question body and 2) available annotations are scarce and fragmented, driven by participants. We design a novel combination of recurrent and convolutional models (gated convolutions) to effectively map questions to their semantic representations. The models are pre-trained within an encoder-decoder framework (from body to title) on the basis of the entire raw corpus, and fine-tuned discriminatively from limited annotations. Our evaluation demonstrates that our model yields 10\% gain over a standard IR baseline, and 6\% over standard neural network architectures (including CNNs and LSTMs) trained analogously.

Connected through hyperedge HE-1 (induced by source paper "scene motion decomposition for learnable visual odometry"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "deeptam deep tracking and mapping"
    - "deep ordinal regression network for monocular depth estimation"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "guided feature selection for deep visual odometry"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "deriving neural architectures from sequence and graph kernels"):
  with members:
    - "lstm a search space odyssey"
    - "recurrent additive networks"
    - "long short term memory networks for machine reading"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "gated graph sequence neural networks"
    - "recurrent neural network grammars"
    - "recurrent highway networks"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-3 (induced by source paper "rationalizing neural predictions"):
  with members:
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "long short term memory networks for machine reading"
    - "why should i trust you explaining the predictions of any classifier"
    - "bag of tricks for efficient text classification"
    - "semi supervised question retrieval with gated convolutions"
    - "visualizing and understanding neural models in nlp"
    - "a neural attention model for abstractive sentence summarization"
    - "show attend and tell neural image caption generation with visual 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.CL(Computation and Language)