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 "deep reasoning with knowledge graph for social relationship understanding"):
  Abstract: Social relationships (e.g., friends, couple etc.) form the basis of the social network in our daily life. Automatically interpreting such relationships bears a great potential for the intelligent systems to understand human behavior in depth and to better interact with people at a social level. Human beings interpret the social relationships within a group not only based on the people alone, and the interplay between such social relationships and the contextual information around the people also plays a significant role. However, these additional cues are largely overlooked by the previous studies. We found that the interplay between these two factors can be effectively modeled by a novel structured knowledge graph with proper message propagation and attention. And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects. Meanwhile, a graph attentional mechanism is introduced to explicitly reason about the discriminative objects to promote recognition. Extensive experiments on the public benchmarks demonstrate the superiority of our method over the existing leading competitors.

Includes members:
  Member "a domain based approach to social relation recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta transfer learning through hard tasks")
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pedestrian attribute recognition a survey")
  Member "dual glance model for deciphering social relationships":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "video storytelling textual summaries for events")
  Member "recurrent attentional reinforcement learning for multi label image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video")
  Member "knowledge guided recurrent neural network learning for task oriented action prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge embedded routing network for scene graph generation")
  Member "hadamard product for low rank bilinear pooling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "component analysis for visual question answering architectures")
  Member "semantic object parsing with graph lstm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "gated graph sequence neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)