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: "sequential image based attention network for inferring force estimation without haptic sensor"
  Abstract: Humans can approximately infer the force of interaction between objects using only visual information because we have learned it through experiences. Based on this idea, in this paper, we propose a method based on a recurrent convolutional neural network that uses sequential images to infer the interaction force without using a haptic sensor. To train and validate deep learning methods, we collected a large number of images and corresponding data concerning the interaction forces between objects shown therein through an electronic motor-based device. To focus on the changing appearances of a target object owing to external force in the images, we develop a sequential image-based attention module that learns a salient model from temporal dynamics for predicting unknown interaction forces. We propose a sequential image-based spatial attention module and a sequential image-based channel attention module, which are extended to exploit multiple images based on corresponding weighted average pooling layers. Extensive experimental results verified that the proposed method can successfully infer interaction forces in various conditions featuring different target materials, changes in illumination, and directions of external forces.

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)