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 "a joint sequence fusion model for video question answering and retrieval"):
  Abstract: We present an approach named JSFusion (Joint Sequence Fusion) that can measure semantic similarity between any pairs of multimodal sequence data (e.g. a video clip and a language sentence). Our multimodal matching network consists of two key components. First, the Joint Semantic Tensor composes a dense pairwise representation of two sequence data into a 3D tensor. Then, the Convolutional Hierarchical Decoder computes their similarity score by discovering hidden hierarchical matches between the two sequence modalities. Both modules leverage hierarchical attention mechanisms that learn to promote well-matched representation patterns while prune out misaligned ones in a bottom-up manner. Although the JSFusion is a universal model to be applicable to any multimodal sequence data, this work focuses on video-language tasks including multimodal retrieval and video QA. We evaluate the JSFusion model in three retrieval and VQA tasks in LSMDC, for which our model achieves the best performance reported so far. We also perform multiple-choice and movie retrieval tasks for the MSR-VTT dataset, on which our approach outperforms many state-of-the-art methods.

Includes members:
  Member "video fill in the blank using lr rl lstms with spatial temporal attentions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter")
  Member "learning from video and text via large scale discriminative clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "tgif qa toward spatio temporal reasoning in visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "hadamard product for low rank bilinear pooling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "component analysis for visual question answering architectures")
  Member "a dataset and exploration of models for understanding video data through fill in the blank question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tvqa spatio temporal grounding for video question answering")
  Member "learning language visual embedding for movie understanding with natural language":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "measuring social biases in grounded vision and language embeddings")
  Member "cnn architectures for large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi modal dense video captioning")
  Member "end to end concept word detection for video captioning retrieval and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)