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: "word embedding perturbation for sentence classification"
  Abstract: In this technique report, we aim to mitigate the overfitting problem of natural language by applying data augmentation methods. Specifically, we attempt several types of noise to perturb the input word embedding, such as Gaussian noise, Bernoulli noise, and adversarial noise, etc. We also apply several constraints on different types of noise. By implementing these proposed data augmentation methods, the baseline models can gain improvements on several sentence classification tasks.

Connected through hyperedge HE-1 (induced by source paper "an attention gated convolutional neural network for sentence classification"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "self normalizing neural networks"
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "dynamic compositional neural networks over tree structure"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "end to end text classification via image based embedding using character level networks"):
  with members:
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "random erasing data augmentation"
    - "learning chinese word representations from glyphs of characters"
    - "learning character level compositionality with visual features"
    - "deconvolutional paragraph representation learning"
    - "language modeling with gated convolutional networks"
    - "quasi recurrent neural networks"
    - "data recombination for neural semantic parsing"

Connected through hyperedge HE-3 (induced by source paper "evolutionary data measures understanding the difficulty of text classification tasks"):
  with members:
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "learning to generate reviews and discovering sentiment"
    - "automated hate speech detection and the problem of offensive language"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "enriching word vectors with subword information"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "bag of tricks for efficient text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"

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)