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 "on the contributions of visual and textual supervision in low resource semantic speech retrieval"):
  Abstract: Recent work has shown that speech paired with images can be used to learn semantically meaningful speech representations even without any textual supervision. In real-world low-resource settings, however, we often have access to some transcribed speech. We study whether and how visual grounding is useful in the presence of varying amounts of textual supervision. In particular, we consider the task of semantic speech retrieval in a low-resource setting. We use a previously studied data set and task, where models are trained on images with spoken captions and evaluated on human judgments of semantic relevance. We propose a multitask learning approach to leverage both visual and textual modalities, with visual supervision in the form of keyword probabilities from an external tagger. We find that visual grounding is helpful even in the presence of textual supervision, and we analyze this effect over a range of sizes of transcribed data sets. With ~5 hours of transcribed speech, we obtain 23% higher average precision when also using visual supervision.

Includes members:
  Member "semantic speech retrieval with a visually grounded model of untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "towards visually grounded sub word speech unit discovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "symbolic inductive bias for visually grounded learning of spoken language":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "a hierarchical multi task approach for learning embeddings from semantic tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "visually grounded cross lingual keyword spotting in speech":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "visually grounded learning of keyword prediction from untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")

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)