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 "awe asymmetric word embedding for textual entailment"):
  Abstract: Textual entailment is a fundamental task in natural language processing. It refers to the directional relation between text fragments such that the "premise" can infer "hypothesis". In recent years deep learning methods have achieved great success in this task. Many of them have considered the inter-sentence word-word interactions between the premise-hypothesis pairs, however, few of them considered the "asymmetry" of these interactions. Different from paraphrase identification or sentence similarity evaluation, textual entailment is essentially determining a directional (asymmetric) relation between the premise and the hypothesis. In this paper, we propose a simple but effective way to enhance existing textual entailment algorithms by using asymmetric word embeddings. Experimental results on SciTail and SNLI datasets show that the learned asymmetric word embeddings could significantly improve the word-word interaction based textual entailment models. It is noteworthy that the proposed AWE-DeIsTe model can get 2.1% accuracy improvement over prior state-of-the-art on SciTail.

Includes members:
  Member "end task oriented textual entailment via deep explorations of inter sentence interactions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching")
  Member "semantic sentence matching with densely connected recurrent and co attentive information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "utilizing bert intermediate layers for aspect based sentiment analysis and natural language inference")
  Member "natural language inference over interaction space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial nli a new benchmark for natural language understanding")
  Member "hierarchical embeddings for hypernymy detection and directionality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learned in translation contextualized word vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "specialising word vectors for lexical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inducing relational knowledge from bert")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")

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)