human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "guide me interacting with deep networks"
  Abstract: Interaction and collaboration between humans and intelligent machines has become increasingly important as machine learning methods move into real-world applications that involve end users. While much prior work lies at the intersection of natural language and vision, such as image captioning or image generation from text descriptions, less focus has been placed on the use of language to guide or improve the performance of a learned visual processing algorithm. In this paper, we explore methods to flexibly guide a trained convolutional neural network through user input to improve its performance during inference. We do so by inserting a layer that acts as a spatio-semantic guide into the network. This guide is trained to modify the network's activations, either directly via an energy minimization scheme or indirectly through a recurrent model that translates human language queries to interaction weights. Learning the verbal interaction is fully automatic and does not require manual text annotations. We evaluate the method on two datasets, showing that guiding a pre-trained network can improve performance, and provide extensive insights into the interaction between the guide and the CNN.

Connected through hyperedge HE-1 (induced by source paper "panoptic image annotation with a collaborative assistant"):
  with members:
    - "panoptic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "blazingly fast video object segmentation with pixel wise metric learning"

Connected through hyperedge HE-2 (induced by source paper "natural vocabulary emerges from free form annotations"):
  with members:
    - "learning to discover and localize visual objects with open vocabulary"
    - "open vocabulary phrase detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "context embedding networks"
    - "teaching machines to describe images via natural language feedback"
    - "who said what modeling individual labelers improves classification"
    - "open vocabulary scene parsing"

Connected through hyperedge HE-3 (induced by source paper "visual reasoning with multi hop feature modulation"):
  with members:
    - "efficient video object segmentation via network modulation"
    - "mattnet modular attention network for referring expression comprehension"
    - "modulating early visual processing by language"
    - "visual question answering datasets algorithms and future challenges"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "comprehension guided referring expressions"
    - "parallel attention a unified framework for visual object discovery through dialogs and queries"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-4 (induced by source paper "interactive full image segmentation by considering all regions jointly"):
  with members:
    - "panoptic segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "extreme clicking for efficient object annotation"

Question: Please tell me which class the center node 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)