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 "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities"):
  Abstract: In the last decade, video blogs (vlogs) have become an extremely popular method through which people express sentiment. The ubiquitousness of these videos has increased the importance of multimodal fusion models, which incorporate video and audio features with traditional text features for automatic sentiment detection. Multimodal fusion offers a unique opportunity to build models that learn from the full depth of expression available to human viewers. In the detection of sentiment in these videos, acoustic and video features provide clarity to otherwise ambiguous transcripts. In this paper, we present a multimodal fusion model that exclusively uses high-level video and audio features to analyze spoken sentences for sentiment. We discard traditional transcription features in order to minimize human intervention and to maximize the deployability of our model on at-scale real-world data. We select high-level features for our model that have been successful in nonaffect domains in order to test their generalizability in the sentiment detection domain. We train and test our model on the newly released CMU Multimodal Opinion Sentiment and Emotion Intensity (CMUMOSEI) dataset, obtaining an F1 score of 0.8049 on the validation set and an F1 score of 0.6325 on the held-out challenge test set.

Includes members:
  Member "multi attention recurrent network for human communication comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis")
  Member "memory fusion network for multi view sequential learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "emotions don t lie a deepfake detection method using audio visual affective cues")
  Member "conversational ai the science behind the alexa prize":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "multimodal sentiment analysis addressing key issues and setting up baselines":
    also connected through other hyperedges:
      (none)
  Member "tensor fusion network for multimodal sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fgn fusion glyph network for chinese named entity recognition")
  Member "adversarial examples for evaluating reading comprehension systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "comparing human and machine errors in conversational speech transcription":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving oov detection and resolution with external language models in acoustic to word asr")
  Member "end to end concept word detection for video captioning retrieval and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips")

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.CV(Computer Vision and Pattern Recognition)