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: "tx ray quantifying and explaining model knowledge transfer in un supervised nlp"
  Abstract: While state-of-the-art NLP explainability (XAI) methods focus on supervised, per-instance end or diagnostic probing task evaluation[4, 2, 10], this is insufficient to interpret and quantify model knowledge transfer during (un-) supervised training. By instead expressing each neuron as an interpretable token-activation distribution collected over many instances, one can quantify and guide visual exploration of neuron-knowledge change between model training stages to analyze transfer beyond probing tasks and the per-instance level. This allows one to analyze: (RQ1) how neurons abstract knowledge during unsupervised pretraining; (RQ2) how pretrained neurons zero-shot transfer knowledge to new domain data; and (RQ3) how supervised tasks reorder pretrained neuron knowledge abstractions. Since the meaningfulness of XAI methods is hard to quantify [11, 4], we analyze three example learning setups (RQ1-3) to empirically verify that our method (TX-Ray): identifies transfer (ir-)relevant neurons for pruning (RQ3), and that its transfer metrics coincide with traditional measures like perplexity (RQ1). We also find, that TX-Ray guided pruning of supervision (ir-)relevant neuron-knowledge (RQ3) can identify `lottery ticket'-like [9, 40] neurons that drive model performance and robustness. Upon inspecting pruned neurons, we find that task-relevant neuron-knowledge (`tickets'), appear (over-)fit, while task-irrelevant neurons lower overfitting, i.e. TX-Ray identifies neurons that generalize, transfer or specialize model-knowledge [25]. Finally, through RQ1-3, we find that TX-Ray helps to explore and quantify dynamics of (continual) knowledge transfer and that it can shed light on neuron-knowledge specialization and generalization, to complement (costly) supervised probing task procurement and established `summary' statistics like perplexity, ROC or F scores.

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.LG(Machine Learning)