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 "scene recognition with prototype agnostic scene layout"):
  Abstract: Abstract--- Exploiting the spatial structure in scene images is a key research direction for scene recognition. Due to the large intra-class structural diversity, building and modeling flexible structural layout to adapt various image characteristics is a challenge. Existing structural modeling methods in scene recognition either focus on predefined grids or rely on learned prototypes, which all have limited representative ability. In this paper, we propose Prototype-agnostic Scene Layout (PaSL) construction method to build the spatial structure for each image without conforming to any prototype. Our PaSL can flexibly capture the diverse spatial characteristic of scene images and have considerable generalization capability. Given a PaSL, we build Layout Graph Network (LGN) where regions in PaSL are defined as nodes and two kinds of independent relations between regions are encoded as edges. The LGN aims to incorporate two topological structures (formed in spatial and semantic similarity dimensions) into image representations through graph convolution. Extensive experiments show that our approach achieves state-of-the-art results on widely recognized MIT67 and SUN397 datasets without multi-model or multi-scale fusion. Moreover, we also conduct the experiments on one of the largest scale datasets, Places365. The results demonstrate the proposed method can be well generalized and obtains competitive performance.

Includes members:
  Member "semantic fisher scores for task transfer using objects to classify scenes":
    also connected through other hyperedges:
      (none)
  Member "zero shot recognition via semantic embeddings and knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "from volcano to toyshop adaptive discriminative region discovery for scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi scale discriminative region discovery for weakly supervised object localization")
  Member "graph r cnn for scene graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "a deeper look at power normalizations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "situation recognition with graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bayesian relational memory for semantic visual navigation")
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "hybrid cnn and dictionary based models for scene recognition and domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic aware scene 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.CV(Computer Vision and Pattern Recognition)