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: "long activity video understanding using functional object oriented network"
  Abstract: Video understanding is one of the most challenging topics in computer vision. In this paper, a four-stage video understanding pipeline is presented to simultaneously recognize all atomic actions and the single on-going activity in a video. This pipeline uses objects and motions from the video and a graph-based knowledge representation network as prior reference. Two deep networks are trained to identify objects and motions in each video sequence associated with an action. Low Level image features are then used to identify objects of interest in that video sequence. Confidence scores are assigned to objects of interest based on their involvement in the action and to motion classes based on results from a deep neural network that classifies the on-going action in video into motion classes. Confidence scores are computed for each candidate functional unit associated with an action using a knowledge representation network, object confidences, and motion confidences. Each action is therefore associated with a functional unit and the sequence of actions is further evaluated to identify the single on-going activity in the video. The knowledge representation used in the pipeline is called the functional object-oriented network which is a graph-based network useful for encoding knowledge about manipulation tasks. Experiments are performed on a dataset of cooking videos to test the proposed algorithm with action inference and activity classification. Experiments show that using functional object oriented network improves video understanding significantly.

Connected through hyperedge HE-1 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "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"
    - "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"
    - "identifying object states in cooking related images"
    - "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"
    - "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-4 (induced by source paper "vgg fine tuning for cooking state recognition"):
  with members:
    - "cooking state recognition from images using inception architecture"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "manipulation motion taxonomy and coding for robots"):
  with members:
    - "a survey of knowledge representation in service robotics"
    - "scaling egocentric vision the epic kitchens dataset"
    - "functional object oriented network construction expansion"
    - "a dataset of daily interactive manipulation"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-6 (induced by source paper "a weighted functional object oriented network for task planning"):
  with members:
    - "a survey of knowledge representation in service robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-7 (induced by source paper "functional object oriented network considering robot s capability in human robot collaboration"):
  with members:
    - "identifying object states in cooking related images"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-8 (induced by source paper "ai meets physical world exploring robot cooking"):
  with members:
    - "a dataset of daily interactive manipulation"
    - "learning to pour"
    - "functional object oriented network for manipulation learning"
    - "datasets on object manipulation and interaction a survey"
    - "generating sequences with recurrent neural networks"

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)