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 "instance based model adaptation for direct speech translation"):
  Abstract: Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available training corpora. We tackle this limitation with a method to improve data exploitation and boost the system's performance at inference time. Our approach allows us to customize "on the fly" an existing model to each incoming translation request. At its core, it exploits an instance selection procedure to retrieve, from a given pool of data, a small set of samples similar to the input query in terms of latent properties of its audio signal. The retrieved samples are then used for an instance-specific fine-tuning of the model. We evaluate our approach in three different scenarios. In all data conditions (different languages, in/out-of-domain adaptation), our instance-based adaptation yields coherent performance gains over static models.

Includes members:
  Member "attention passing models for robust and data efficient end to end speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one to many multilingual end to end speech translation")
  Member "non parametric adaptation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simple scalable adaptation for neural machine translation")
  Member "end to end speech translation with knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multilingual end to end speech translation")
  Member "tied multitask learning for neural speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "end to end automatic speech translation of audiobooks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "pre training on high resource speech recognition improves low resource speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual end to end speech translation")
  Member "the ustc nel speech translation system at iwslt 2018":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comparative study on end to end speech to text translation")
  Member "leveraging weakly supervised data to improve end to end speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual end to end speech translation")

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)