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 "symbolic inductive bias for visually grounded learning of spoken language"):
  Abstract: A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.

Includes members:
  Member "parameter sharing between dependency parsers for related languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "lessons learned in multilingual grounded language learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "countering language drift via visual grounding")
  Member "the natural language decathlon multitask learning as question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "syntactic scaffolds for semantic structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalizing natural language analysis through span relation representations")
  Member "sequence based multi lingual low resource speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards zero shot learning for automatic phonemic transcription")
  Member "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "jointly discovering visual objects and spoken words from raw sensory input":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "scheduled multi task learning from syntax to translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to multi task learn for better neural machine 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)