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: "mathematics content understanding for cyberlearning via formula evolution map"
  Abstract: Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula. In this paper, we propose a novel problem, ``mathematics content understanding'', for cyberlearning and cyberreading. To address this problem, we create a Formula Evolution Map (FEM) offline and implement a novel online learning/reading environment, PDF Reader with Math-Assistant (PRMA), which incorporates innovative math-scaffolding methods. The proposed algorithm/system can auto-characterize student emerging math-information need while reading a paper and enable students to readily explore the formula evolution trajectory in FEM. Based on a math-information need, PRMA utilizes innovative joint embedding, formula evolution mining, and heterogeneous graph mining algorithms to recommend high quality Open Educational Resources (OERs), e.g., video, Wikipedia page, or slides, to help students better understand the math-content in the paper. Evaluation and exit surveys show that the PRMA system and the proposed formula understanding algorithm can effectively assist master and PhD students better understand the complex math-content in the class readings.

Connected through hyperedge HE-1 (induced by source paper "cross domain aspect category transfer and detection via traceable heterogeneous graph representation learning"):
  with members:
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "attention is all you need"
    - "recent trends in deep learning based natural language processing"
    - "aspect level sentiment classification with deep memory network"
    - "node2vec scalable feature learning for networks"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "semi supervised classification with graph convolutional networks"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"):
  with members:
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "learning chinese word representations from glyphs of characters"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (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"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "attention is all you need"
    - "preliminary exploration of formula embedding for mathematical information retrieval can mathematical formulae be embedded like a natural language"
    - "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"

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)