human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "episodic memory in lifelong language learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "right for the wrong reasons diagnosing syntactic heuristics in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial filters of dataset biases")
  Member "are sixteen heads really better than one":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "to tune or not to tune adapting pretrained representations to diverse tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "contextual lensing of universal sentence representations")
  Member "visual interaction with deep learning models through collaborative semantic inference":
    also connected through other hyperedges:
      (none)
  Member "universal adversarial triggers for attacking and analyzing nlp":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")

Question: Please tell me which class the source paper 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)