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 "pruning a bert based question answering model"):
  Abstract: We investigate compressing a BERT-based question answering system by pruning parameters from the underlying BERT model. We start from models trained for SQuAD 2.0 and introduce gates that allow selected parts of transformers to be individually eliminated. Specifically, we investigate (1) reducing the number of attention heads in each transformer, (2) reducing the intermediate width of the feed-forward sublayer of each transformer, and (3) reducing the embedding dimension. We compare several approaches for determining the values of these gates. We find that a combination of pruning attention heads and the feed-forward layer almost doubles the decoding speed, with only a 1.5 f-point loss in accuracy.

Includes members:
  Member "are sixteen heads really better than one":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "reducing transformer depth on demand with structured dropout":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking batch normalization in transformers")
  Member "distilbert a distilled version of bert smaller faster cheaper and lighter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "small and practical bert models for sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "q bert hessian based ultra low precision quantization of bert":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "structured pruning of large language models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search")
  Member "well read students learn better on the importance of pre training compact models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")

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)