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 "rethinking cooperative rationalization introspective extraction and complement control"):
  Abstract: Selective rationalization has become a common mechanism to ensure that predictive models reveal how they use any available features. The selection may be soft or hard, and identifies a subset of input features relevant for prediction. The setup can be viewed as a co-operate game between the selector (aka rationale generator) and the predictor making use of only the selected features. The co-operative setting may, however, be compromised for two reasons. First, the generator typically has no direct access to the outcome it aims to justify, resulting in poor performance. Second, there's typically no control exerted on the information left outside the selection. We revise the overall co-operative framework to address these challenges. We introduce an introspective model which explicitly predicts and incorporates the outcome into the selection process. Moreover, we explicitly control the rationale complement via an adversary so as not to leave any useful information out of the selection. We show that the two complementary mechanisms maintain both high predictive accuracy and lead to comprehensive rationales.

Includes members:
  Member "guiding extractive summarization with question answering rewards":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modelling latent skills for multitask language generation")
  Member "interpretable neural predictions with differentiable binary variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning world graphs to accelerate hierarchical reinforcement learning")
  Member "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning from explanations with neural execution tree")
  Member "game theoretic interpretability for temporal modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "functional transparency for structured data a game theoretic approach")
  Member "towards robust interpretability with self explaining neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards a unified evaluation of explanation methods without ground truth")
  Member "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a just and comprehensive strategy for using nlp to address online abuse")
  Member "l shapley and c shapley efficient model interpretation for structured data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "importance driven deep learning system testing")

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.CL(Computation and Language)