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: "cooking state recognition from images using inception architecture"
  Abstract: A kitchen robot properly needs to understand the cooking environment to continue any cooking activities. But object’s state detection has not been researched well so far as like object detection. In this paper, we propose a deep learning approach to identify different cooking states from images for a kitchen robot. In our research, we investigate particularly the performance of Inception architecture and propose a modified architecture based on Inception model to classify different cooking states. The model is analyzed robustly in terms of different layers, and optimizers. Experimental results on a cooking dataset demonstrate that proposed model can be a potential solution to the cooking state recognition problem.

Connected through hyperedge HE-1 (induced by source paper "tuned inception v3 for recognizing states of cooking ingredients"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "images and recipes retrieval in the cooking context"
    - "a dataset of daily interactive manipulation"
    - "classifying cooking object s state using a tuned vgg convolutional neural network"
    - "functional object oriented network for manipulation learning"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "vgg fine tuning for cooking state recognition"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "fine tuning vgg neural network for fine grained state recognition of food images"
    - "functional object oriented network for manipulation learning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)