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 "learning from multiview correlations in open domain videos"):
  Abstract: An increasing number of datasets contain multiple views, such as video, sound and automatic captions. A basic challenge in representation learning is how to leverage multiple views to learn better representations. This is further complicated by the existence of a latent alignment between views, such as between speech and its transcription, and by the multitude of choices for the learning objective. We explore an advanced, correlation-based representation learning method on a 4-way parallel, multimodal dataset, and assess the quality of the learned representations on retrieval-based tasks. We show that the proposed approach produces rich representations that capture most of the information shared across views. Our best models for speech and textual modalities achieve retrieval rates from 70.7% to 96.9% on open-domain, user-generated instructional videos. This shows it is possible to learn reliable representations across disparate, unaligned and noisy modalities, and encourages using the proposed approach on larger datasets.

Includes members:
  Member "unsupervised cross modal alignment of speech and text embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "representation learning for electronic health records")
  Member "how2 a large scale dataset for multimodal language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "looking enhances listening recovering missing speech using images")
  Member "learning joint multilingual sentence representations with neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "absent cross lingual sentence representation mapping with bidirectional gans")
  Member "learning word like units from joint audio visual analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech")
  Member "objects that sound":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "deep generalized canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "multimodal machine learning a survey and taxonomy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

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.LG(Machine Learning)