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: "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.

Connected through hyperedge HE-1 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"

Connected through hyperedge HE-2 (induced by source paper "joint object and state recognition using language knowledge"):
  with members:
    - "a survey of knowledge representation in service robotics"
    - "long activity video understanding using functional object oriented network"
    - "functional object oriented network construction expansion"
    - "joint discovery of object states and manipulation actions"
    - "functional object oriented network for manipulation learning"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "boosting image captioning with attributes"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-3 (induced by source paper "tuned inception v3 for recognizing states of cooking ingredients"):
  with members:
    - "cooking state recognition from images using inception architecture"
    - "long activity video understanding using functional object oriented network"
    - "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-4 (induced by source paper "vgg fine tuning for cooking state recognition"):
  with members:
    - "cooking state recognition from images using inception architecture"
    - "long activity video understanding using functional object oriented network"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "cooking state recognition from images using inception architecture"):
  with members:
    - "functional object oriented network for manipulation learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

Connected through hyperedge HE-6 (induced by source paper "synthetic image augmentation for improved classification using generative adversarial networks"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "towards adversarial retinal image synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "a weighted functional object oriented network for task planning"):
  with members:
    - "a survey of knowledge representation in service robotics"
    - "long activity video understanding using functional object oriented network"
    - "functional object oriented network construction expansion"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-8 (induced by source paper "functional object oriented network considering robot s capability in human robot collaboration"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "functional object oriented network for manipulation learning"

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)