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: "finding the topic of a set of images"
  Abstract: In this paper we introduce the problem of determining the topic that a set of images is describing, where every topic is represented as a set of words. Different from other problems like tag assignment or similar, a) we assume multiple images are used as input instead of single image, b) Input images are typically not visually related, c) Input images are not necessarily semantically close, and d) Output word space is unconstrained. In our proposed solution, visual information of each query image is used to retrieve similar images with text labels (tags) from an image database. We consider a scenario where the tags are very noisy and diverse, given that they were obtained by implicit crowd-sourcing in a database of 1 million images and over seventy seven thousand tags. The words or tags associated to each query are processed jointly in a word selection algorithm using random walks that allows to refine the search topic, rejecting words that are not part of the topic and produce a set of words that fairly describe the topic. Experiments on a dataset of 300 topics, with up to twenty images per topic, show that our algorithm performs better than the proposed baseline for any number of query images. We also present a new Conditional Random Field (CRF) word mapping algorithm that preserves the semantic similarity of the mapped words, increasing the performance of the results over the baseline.

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)