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: "digit recognition in handwritten weather records"
  Abstract: Abstract. This paper addresses the automatic recognition of handwritten tem-peraturevaluesinweatherrecords. Thelocalizationoftablecellsisbasedon line detection using projection proﬁles. Further, a stroke-preservinglineremovalmethodwhichisbasedongradientimagesisproposed. Thepresented digit recognition utilizes features which are extracted using aset of ﬁlters and a Support Vector Machine classiﬁer. It was evaluatedontheMNISTandtheUSPSdatasetandourowndatabasewithabout17,000RGBdigitimages. Anaccuracyof99.36%perdigitisachievedfortheentiresystemusingasetof84weatherrecords. 1 Introduction Inadditiontohandwrittendocumentsstoredinhistoricarchivestherearepro-cesses, like manually ﬁlling out forms, which still depend on pen and paper.However, accessing the information stored in those documents requires timeand manpower [9]. By digitizing those documents, the advantages of digitallystored information, such as ease of access, can be exploited for handwrittendocuments[9].Thedocumentscoveredinthispaper,i.e. weatherrecords,consistofknownprintedformswithhandwritteninformation. Eventhoughthestructureoftheform is known beforehand, due to prior handling and the scanning process,global and local deformations are introduced. Further, the structure of formssuggestscertainareaswhichareapplicableforhandwriting,yetwritersarenotbound to this constraint. Additionally, handwriting varies signiﬁcantly dueto diﬀerent writing styles. Even the handwriting of a single writer exhibitsvariations. In Figure 1 an example of a weather record is shown. The originsoftheweatherrecordsregardedinthispaperareseveralmeasurementstationslocatedinAustria(LowerAustria).Figure1: WeatherrecordfromFebruaryoftheyear2000fromameasurementstation in Lower Austria. The temperature values are located in the column"Lufttemperatur

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)