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 "visual semantic graph attention networks for human object interaction detection"):
  Abstract: In scene understanding, machines benefit from not only detecting individual scene instances but also from learning their possible interactions. Human-Object Interaction (HOI) Detection infers the action predicate on a   triplet. Contextual information has been found critical in inferring interactions. However, most works only use local features from a single subject-object pair for inference. Few works have studied the disambiguating contribution of subsidiary relations made available via graph networks and the impact attention mechanisms have in inference. Similarly, few have learned to effectively leverage visual cues along with the intrinsic semantic regularities contained in HOIs. We contribute a dual-graph attention network that effectively aggregates contextual visual, spatial, and semantic information dynamically from primary subject-object relations as well as subsidiary relations through attention mechanisms for strong disambiguating power. The network learns to use both primary and subsidiary relations to improve inference in challenging settings: encouraging the right interpretations and discouraging incorrect ones. We call our model: Visual-Semantic Graph Attention Networks (VS-GATs). We surpass state-of-the-art HOI detection mAPs in the challenging HICO-DET dataset, including in long-tail cases that are harder to interpret. Code, video, and supplementary information are available at this http URL

Includes members:
  Member "actional structural graph convolutional networks for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "pose aware multi level feature network for human object interaction detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascaded human object interaction recognition")
  Member "deep graph library towards efficient and scalable deep learning on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards graph representation learning in emergent communication")
  Member "detecting human object interactions via functional generalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "classifying all interacting pairs in a single shot")
  Member "relational action forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning video representations using contrastive bidirectional transformer")
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "3d human pose estimation in video with temporal convolutions and semi supervised training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "ican instance centric attention network for human object interaction detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cascaded human object interaction recognition")

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.LG(Machine Learning)