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: "mathematical language processing project"
  Abstract: In natural language, words and phrases themselves imply the semantics. In contrast, the meaning of identifiers in mathematical formulae is undefined. Thus scientists must study the context to decode the meaning. The Mathematical Language Processing (MLP) project aims to support that process. In this paper, we compare two approaches to discover identifier-definition tuples. At first we use a simple pattern matching approach. Second, we present the MLP approach that uses part-of-speech tag based distances as well as sentence positions to calculate identifier-definition probabilities. The evaluation of our prototypical system, applied on the Wikipedia text corpus, shows that our approach augments the user experience substantially. While hovering the identifiers in the formula, tool-tips with the most probable definitions occur. Tests with random samples show that the displayed definitions provide a good match with the actual meaning of the identifiers.

Connected through hyperedge HE-1 (induced by source paper "topiceq a joint topic and mathematical equation model for scientific texts"):
  with members:
    - "topic compositional neural language model"
    - "a neural knowledge language model"
    - "topically driven neural language model"
    - "discovering discrete latent topics with neural variational inference"
    - "equation parsing mapping sentences to grounded equations"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "image to markup generation with coarse to fine attention"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-2 (induced by source paper "identifier namespaces in mathematical notation"):
  with members:
    - "micro clustering finding small clusters in large diversity"
    - "morphological analyzer and generator for russian and ukrainian languages"
    - "pos tagging and its applications for mathematics"
    - "efficient estimation of word representations in vector space"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-3 (induced by source paper "growing the digital repository of mathematical formulae with generic latex sources"):
  with members:
    - "digital repository of mathematical formulae"
    - "which one is better presentation based or content based math search"
    - "mathoid robust scalable fast and accessible math rendering for wikipedia"

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.DL(Digital Libraries)