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 "stories in the eye contextual visual interactions for efficient video to language translation"):
  Abstract: Integrating higher level visual and linguistic interpretations is at the heart of human intelligence. As automatic visual category recognition in images is approaching human performance, the high level understanding in the dynamic spatiotemporal domain of videos and its translation into natural language is still far from being solved. While most works on vision-to-text translations use pre-learned or pre-established computational linguistic models, in this paper we present an approach that uses vision alone to efficiently learn how to translate into language the video content. We discover, in simple form, the story played by main actors, while using only visual cues for representing objects and their interactions. Our method learns in a hierarchical manner higher level representations for recognizing subjects, actions and objects involved, their relevant contextual background and their interaction to one another over time. We have a three stage approach: first we take in consideration features of the individual entities at the local level of appearance, then we consider the relationship between these objects and actions and their video background, and third, we consider their spatiotemporal relations as inputs to classifiers at the highest level of interpretation. Thus, our approach finds a coherent linguistic description of videos in the form of a subject, verb and object based on their role played in the overall visual story learned directly from training data, without using a known language model. We test the efficiency of our approach on a large scale dataset containing YouTube clips taken in the wild and demonstrate state-of-the-art performance, often superior to current approaches that use more complex, pre-learned linguistic knowledge.

Includes members:
  Member "coherent multi sentence video description with variable level of detail":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal dense video captioning")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "video in sentences out":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "translating videos to natural language using deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "rich feature hierarchies for accurate object detection and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)