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: "functional object oriented network for manipulation learning"
  Abstract: This paper presents a novel structured knowledge representation called the functional object-oriented network (FOON) to model the connectivity of the functional-related objects and their motions in manipulation tasks. The graphical model FOON is learned by observing object state change and human manipulations with the objects. Using a well-trained FOON, robots can decipher a task goal, seek the correct objects at the desired states on which to operate, and generate a sequence of proper manipulation motions. The paper describes FOON's structure and an approach to form a universal FOON with extracted knowledge from online instructional videos. A graph retrieval approach is presented to generate manipulation motion sequences from the FOON to achieve a desired goal, demonstrating the flexibility of FOON in creating a novel and adaptive means of solving a problem using knowledge gathered from multiple sources. The results are demonstrated in a simulated environment to illustrate the motion sequences generated from the FOON to carry out the desired tasks.

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"
    - "identifying object states in cooking related images"
    - "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"

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"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "joint discovery of object states and manipulation actions"
    - "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"
    - "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"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "robot learning and execution of collaborative manipulation plans from youtube videos"):
  with members:
    - "procedure planning in instructional videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "unsupervised learning and segmentation of complex activities from video"
    - "yolov3 an incremental improvement"
    - "functional object oriented network construction expansion"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"

Connected through hyperedge HE-5 (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"
    - "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"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "accurate robotic pouring for serving drinks"):
  with members:
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "functional object oriented network construction expansion"
    - "a dataset of daily interactive manipulation"
    - "learning to pour"
    - "visual closed loop control for pouring liquids"
    - "synthesis of recurrent neural networks for dynamical system simulation"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "cooking state recognition from images using inception architecture"):
  with members:
    - "identifying object states in cooking related images"
    - "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-8 (induced by source paper "manipulation motion taxonomy and coding for robots"):
  with members:
    - "a survey of knowledge representation in service robotics"
    - "long activity video understanding using functional object oriented network"
    - "scaling egocentric vision the epic kitchens dataset"
    - "functional object oriented network construction expansion"
    - "a dataset of daily interactive manipulation"

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.RO(Robotics)