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: "preliminary exploration of formula embedding for mathematical information retrieval can mathematical formulae be embedded like a natural language"
  Abstract: While neural network approaches are achieving breakthrough performance in the natural language related fields, there have been few similar attempts at mathematical language related tasks. In this study, we explore the potential of applying neural representation techniques to Mathematical Information Retrieval (MIR) tasks. In more detail, we first briefly analyze the characteristic differences between natural language and mathematical language. Then we design a "symbol2vec" method to learn the vector representations of formula symbols (numbers, variables, operators, functions, etc.) Finally, we propose a "formula2vec" based MIR approach and evaluate its performance. Preliminary experiment results show that there is a promising potential for applying formula embedding models to mathematical language representation and MIR tasks.

Connected through hyperedge HE-1 (induced by source paper "discovering mathematical objects of interest a study of mathematical notations"):
  with members:
    - "topiceq a joint topic and mathematical equation model for scientific texts"
    - "improving academic plagiarism detection for stem documents by analyzing mathematical content and citations"
    - "improving the representation and conversion of mathematical formulae by considering their textual context"

Connected through hyperedge HE-2 (induced by source paper "automatic generation of headlines for online math questions"):
  with members:
    - "pattern generation strategies for improving recognition of handwritten mathematical expressions"
    - "self attentive model for headline generation"
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "equation parsing mapping sentences to grounded equations"
    - "image to markup generation with coarse to fine attention"

Connected through hyperedge HE-3 (induced by source paper "why machines cannot learn mathematics yet"):
  with members:
    - "topiceq a joint topic and mathematical equation model for scientific texts"
    - "deep contextualized word representations"
    - "enriching word vectors with subword information"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"

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.IR(Information Retrieval)