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 "cause learning granger causality from event sequences using attribution methods"):
  Abstract: We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited model flexibility or poor model explainability and thus fails to uncover Granger causality across a wide variety of event sequences with diverse event interdependency. To address these weaknesses, we propose CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework for the studied task. The key idea of CAUSE is to first implicitly capture the underlying event interdependency by fitting a neural point process, and then extract from the process a Granger causality statistic using an axiomatic attribution method. Across multiple datasets riddled with diverse event interdependency, we demonstrate that CAUSE achieves superior performance on correctly inferring the inter-type Granger causality over a range of state-of-the-art methods.

Includes members:
  Member "is attention interpretable":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "attention is not explanation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "axiomatic attribution for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "learning important features through propagating activation differences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance driven deep learning system testing")
  Member "towards better understanding of gradient based attribution methods for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")
  Member "the neural hawkes process a neurally self modulating multivariate point process":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "d ej a vu a contextualized temporal attention mechanism for sequential recommendation")
  Member "learning granger causality for hawkes processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "show attend and tell neural image caption generation with visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)