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 robust representations of text"
  Abstract: Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks. We present a regularization based method for limiting network sensitivity to its inputs, inspired by ideas from computer vision, thus learning models that are more robust. Empirical evaluation over a range of sentiment datasets with a convolutional neural network shows that, compared to a baseline model and the dropout method, our method achieves superior performance over noisy inputs and out-of-domain data.

Connected through hyperedge HE-1 (induced by source paper "textbugger generating adversarial text against real world applications"):
  with members:
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "universal sentence encoder"
    - "generating adversarial examples with adversarial networks"
    - "adversarial texts with gradient methods"
    - "towards crafting text adversarial samples"

Connected through hyperedge HE-2 (induced by source paper "second order word embeddings from nearest neighbor topological features"):
  with members:
    - "implicitly incorporating morphological information into word embedding"
    - "the interplay of semantics and morphology in word embeddings"
    - "cross lingual models of word embeddings an empirical comparison"
    - "long short term memory networks for machine reading"
    - "the role of context types and dimensionality in learning word embeddings"
    - "node2vec scalable feature learning for networks"
    - "ultradense word embeddings by orthogonal transformation"
    - "understanding neural networks through representation erasure"

Connected through hyperedge HE-3 (induced by source paper "how robust are character based word embeddings in tagging and mt against wrod scramlbing or randdm nouse"):
  with members:
    - "data noising as smoothing in neural network language models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "multilingual language processing from bytes"
    - "character based neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "neural machine translation of rare words with subword units"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)