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 "dmcl distillation multiple choice learning for multimodal action recognition"):
  Abstract: In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time. Our goal is to leverage the complementary information of multiple modalities to the benefit of the ensemble and each individual network. We introduce a novel Distillation Multiple Choice Learning framework for multimodal data, where different modality networks learn in a cooperative setting from scratch, strengthening one another. The modality networks learned using our method achieve significantly higher accuracy than if trained separately, due to the guidance of other modalities. We evaluate this approach on three video action recognition benchmark datasets. We obtain state-of-the-art results in comparison to other approaches that work with missing modalities at test time.

Includes members:
  Member "unifying heterogeneous classifiers with distillation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unifying specialist image embedding into universal image embedding")
  Member "ntu rgb d 120 a large scale benchmark for 3d human activity understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "learning with privileged information via adversarial discriminative modality distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "videossl semi supervised learning for video classification")
  Member "human action recognition and prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "snapshot distillation teacher student optimization in one generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "long short term sample distillation")
  Member "action recognition for depth video using multi view dynamic images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics")
  Member "motion feature network fixed motion filter for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic inference a new approach toward efficient video action recognition")
  Member "modality distillation with multiple stream networks for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "input dropout for spatially aligned modalities")

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)