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 "analyzing utility of visual context in multimodal speech recognition under noisy conditions"):
  Abstract: Multimodal learning allows us to leverage information from multiple sources (visual, acoustic and text), similar to our experience of the real world. However, it is currently unclear to what extent auxiliary modalities improve performance over unimodal models, and under what circumstances the auxiliary modalities are useful. We examine the utility of the auxiliary visual context in Multimodal Automatic Speech Recognition in adversarial settings, where we deprive the models from partial audio signal during inference time. Our experiments show that while MMASR models show significant gains over traditional speech-to-text architectures (upto 4.2% WER improvements), they do not incorporate visual information when the audio signal has been corrupted. This shows that current methods of integrating the visual modality do not improve model robustness to noise, and we need better visually grounded adaptation techniques.

Includes members:
  Member "probing the need for visual context in multimodal machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t take the easy way out ensemble based methods for avoiding known dataset biases")
  Member "modality attention for end to end audio visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "harnessing gans for zero shot learning of new classes in visual speech recognition")
  Member "how2 a large scale dataset for multimodal language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "looking enhances listening recovering missing speech using images")
  Member "visual features for context aware speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "looking enhances listening recovering missing speech using images")
  Member "nmtpy a flexible toolkit for advanced neural machine translation systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "looking enhances listening recovering missing speech using images")
  Member "attention strategies for multi source sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research")
  Member "lipnet end to end sentence level lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")
  Member "lip reading sentences in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lipreading using temporal convolutional networks")

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)