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 "deeply supervised multimodal attentional translation embeddings for visual relationship detection"):
  Abstract: Detecting visual relationships, i.e.   triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embeddings, where the visual features of each branch are driven by a multimodal attentional mechanism that exploits spatio-linguistic similarities in a low-dimensional space. We present a variety of experiments comparing against all related approaches in the literature, as well as by re-implementing and fine-tuning several of them. Results on the commonly employed VRD dataset [1] show that the proposed method clearly outperforms all others, while we also justify our claims both quantitatively and qualitatively.

Includes members:
  Member "shuffle then assemble learning object agnostic visual relationship features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graphical contrastive losses for scene graph parsing")
  Member "context dependent diffusion network for visual relationship detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visual relationship detection with relative location mining")
  Member "zoom net mining deep feature interactions for visual relationship recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased scene graph generation from biased training")
  Member "detecting visual relationships using box attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions")
  Member "large scale visual relationship understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "an interpretable model for scene graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differentiable scene graphs")
  Member "improving visual relationship detection using semantic modeling of scene descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "debate dynamics for human comprehensible fact checking on knowledge graphs")
  Member "visual relationship detection based on guided proposals and semantic knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unbiased scene graph generation via rich and fair semantic extraction")

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)