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 "identifying object states in cooking related images"):
  Abstract: Understanding object states is as important as object recognition for robotic task planning and manipulation. This paper explicitly introduces and addresses the state identification problem in computer vision for the first time. In this paper, objects and ingredients in cooking videos are explored and the most frequent objects are analyzed. Eleven states from the most frequent cooking objects are examined and a dataset of images containing those objects and their states is created. As a solution to the state identification problem, a Resnet based deep model is proposed. The model is initialized with Imagenet weights and trained on the dataset of eleven classes. The trained state identification model is evaluated on a subset of the Imagenet dataset and state labels are provided using a combination of the model with manual checking. Moreover, an individual model is fine-tuned for each object in the dataset using the initially trained model and object-specific images, where significant improvement is demonstrated.

Includes members:
  Member "functional object oriented network for manipulation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of knowledge representation in service robotics")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "boosting image captioning with attributes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "deepfood deep learning based food image recognition for computer aided dietary assessment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task self supervised learning for human activity detection")
  Member "recognizing fine grained and composite activities using hand centric features and script data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "captioning images with diverse objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unshuffling data for improved generalization")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)