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 "what do deep networks like to read"):
  Abstract: Recent research towards understanding neural networks probes models in a top-down manner, but is only able to identify model tendencies that are known a priori. We propose Susceptibility Identification through Fine-Tuning (SIFT), a novel abstractive method that uncovers a model's preferences without imposing any prior. By fine-tuning an autoencoder with the gradients from a fixed classifier, we are able to extract propensities that characterize different kinds of classifiers in a bottom-up manner. We further leverage the SIFT architecture to rephrase sentences in order to predict the opposing class of the ground truth label, uncovering potential artifacts encoded in the fixed classification model. We evaluate our method on three diverse tasks with four different models. We contrast the propensities of the models as well as reproduce artifacts reported in the literature.

Includes members:
  Member "on measuring social biases in sentence encoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  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 "winogrande an adversarial winograd schema challenge at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "annotation artifacts in natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial filters of dataset biases")
  Member "hypothesis only baselines in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial filters of dataset biases")
  Member "analysis methods in neural language processing a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a kernel of truth determining rumor veracity on twitter by diffusion pattern alone")
  Member "what do deep networks like to see":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lip local importance based pooling")
  Member "swag a large scale adversarial dataset for grounded commonsense inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "natural language qa approaches using reasoning with external knowledge")

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)