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 "a game theoretic approach to class wise selective rationalization"):
  Abstract: Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate. The selection can be optimized post-hoc for trained models or incorporated directly into the method itself (self-explaining). However, an overall selection does not properly capture the multi-faceted nature of useful rationales such as pros and cons for decisions. To this end, we propose a new game theoretic approach to class-dependent rationalization, where the method is specifically trained to highlight evidence supporting alternative conclusions. Each class involves three players set up competitively to find evidence for factual and counterfactual scenarios. We show theoretically in a simplified scenario how the game drives the solution towards meaningful class-dependent rationales. We evaluate the method in single- and multi-aspect sentiment classification tasks and demonstrate that the proposed method is able to identify both factual (justifying the ground truth label) and counterfactual (countering the ground truth label) rationales consistent with human rationalization. The code for our method is publicly available.

Includes members:
  Member "functional transparency for structured data a game theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "composing molecules with multiple property constraints")
  Member "towards robust locally linear deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flexible and context specific ai explainability a multidisciplinary approach")
  Member "rethinking cooperative rationalization introspective extraction and complement control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models")
  Member "towards robust interpretability with self explaining neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards a unified evaluation of explanation methods without ground truth")
  Member "l shapley and c shapley efficient model interpretation for structured data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on completeness aware concept based explanations in deep neural networks")
  Member "learning to explain an information theoretic perspective on model interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")
  Member "deriving machine attention from human rationales":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "text classification with lexicon from preattention mechanism")
  Member "inferring and executing programs for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised question decomposition for question answering")

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)