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 construction expansion"
  Abstract: We build upon the functional object-oriented network (FOON), a structured knowledge representation which is constructed from observations of human activities and manipulations. A FOON can be used for representing object-motion affordances. Knowledge retrieval through graph search allows us to obtain novel manipulation sequences using knowledge spanning across many video sources, hence the novelty in our approach. However, we are limited to the sources collected. To further improve the performance of knowledge retrieval as a follow up to our previous work, we discuss generalizing knowledge to be applied to objects which are similar to what we have in FOON without manually annotating new sources of knowledge. We discuss two means of generalization: 1) expanding our network through the use of object similarity to create new functional units from those we already have, and 2) compressing the functional units by object categories rather than specific objects. We discuss experiments which compare the performance of our knowledge retrieval algorithm with both expansion and compression by categories.

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"
    - "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"
    - "identifying object states in cooking related images"
    - "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"
    - "identifying object states in cooking related images"
    - "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 "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"
    - "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"
    - "towards automatic learning of procedures from web instructional videos"

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"
    - "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-6 (induced by source paper "accurate robotic pouring for serving drinks"):
  with members:
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "a dataset of daily interactive manipulation"
    - "learning to pour"
    - "functional object oriented network for manipulation learning"
    - "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 "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"
    - "a dataset of daily interactive manipulation"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-8 (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"
    - "identifying object states in cooking related images"
    - "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.RO(Robotics)