human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "resilient combination of complementary cnn and rnn features for text classification through attention and ensembling"):
  Abstract: State-of-the-art methods for text classification include several distinct steps of pre-processing, feature extraction and post-processing. In this work, we focus on end-to-end neural architectures and show that the best performance in text classification is obtained by combining information from different neural modules. Concretely, we combine convolution, recurrent and attention modules with ensemble methods and show that they are complementary. We introduce ECGA, an end-to-end go-to architecture for novel text classification tasks. We prove that it is efficient and robust, as it attains or surpasses the state-of-the-art on varied datasets, including both low and high data regimes.

Includes members:
  Member "fine tuned language models for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "deep forest towards an alternative to deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast dense residual network enhancing global dense feature flow for text recognition")
  Member "get to the point summarization with pointer generator networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "comparative study of cnn and rnn for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "a structured self attentive sentence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "joint rnn model for argument component boundary detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi task learning for argumentation mining")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bag of tricks for efficient text classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "description based text classification with reinforcement learning")

Question: Please tell me which class the source paper 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)