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 "human acceptability judgements for extractive sentence compression"):
  Abstract: Recent approaches to English-language sentence compression rely on parallel corpora consisting of sentence-compression pairs. However, a sentence may be shortened in many different ways, which each might be suited to the needs of a particular application. Therefore, in this work, we collect and model crowdsourced judgements of the acceptability of many possible sentence shortenings. We then show how a model of such judgements can be used to support a flexible approach to the compression task. We release our model and dataset for future work.

Includes members:
  Member "neural network acceptability judgments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "sentence level fluency evaluation references help but can be spared":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "a thorough examination of the cnn daily mail reading comprehension task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "a neural attention model for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "a large annotated corpus for learning natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)