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 "evaluating compositionality in sentence embeddings"):
  Abstract: An important frontier in the quest for human-like AI is compositional semantics: how do we design systems that understand an infinite number of expressions built from a finite vocabulary? Recent research has attempted to solve this problem by using deep neural networks to learn vector space embeddings of sentences, which then serve as input to supervised learning problems like paraphrase detection and sentiment analysis. Here we focus on 'natural language inference' (NLI) as a critical test of a system's capacity for semantic compositionality. In the NLI task, sentence pairs are assigned one of three categories: entailment, contradiction, or neutral. We present a new set of NLI sentence pairs that cannot be solved using only word-level knowledge and instead require some degree of compositionality. We use state of the art sentence embeddings trained on NLI (InferSent, Conneau et al. (2017)), and find that performance on our new dataset is poor, indicating that the representations learned by this model fail to capture the needed compositionality. We analyze some of the decision rules learned by InferSent and find that they are largely driven by simple heuristics at the word level that are ecologically valid in the SNLI dataset on which InferSent is trained. Further, we find that augmenting the training dataset with our new dataset improves performance on a held-out test set without loss of performance on the SNLI test set. This highlights the importance of structured datasets in better understanding, as well as improving the performance of, AI systems.

Includes members:
  Member "supervised learning of universal sentence representations from natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "why should i trust you explaining the predictions of any classifier":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "building machines that learn and think like people":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "learning distributed representations of sentences from unlabelled data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual lensing of universal sentence representations")
  Member "skip thought vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "a large annotated corpus for learning natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextual lensing of universal sentence representations")

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)