human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "interpretable neural predictions with differentiable binary variables"
  Abstract: The success of neural networks comes hand in hand with a desire for more interpretability. We focus on text classifiers and make them more interpretable by having them provide a justification, a rationale, for their predictions. We approach this problem by jointly training two neural network models: a latent model that selects a rationale (i.e. a short and informative part of the input text), and a classifier that learns from the words in the rationale alone. Previous work proposed to assign binary latent masks to input positions and to promote short selections via sparsity-inducing penalties such as L0 regularisation. We propose a latent model that mixes discrete and continuous behaviour allowing at the same time for binary selections and gradient-based training without REINFORCE. In our formulation, we can tractably compute the expected value of penalties such as L0, which allows us to directly optimise the model towards a pre-specified text selection rate. We show that our approach is competitive with previous work on rationale extraction, and explore further uses in attention mechanisms.

Connected through hyperedge HE-1 (induced by source paper "learning world graphs to accelerate hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "world discovery models"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "hierarchical policy learning is sensitive to goal space design"
    - "model based reinforcement learning for atari"
    - "discovering options for exploration by minimizing cover time"
    - "keyin discovering subgoal structure with keyframe based video prediction"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "a latent morphology model for open vocabulary neural machine translation"):
  with members:
    - "on the importance of word boundaries in character level neural machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "are all languages equally hard to language model"
    - "character level models versus morphology in semantic role labeling"
    - "from characters to words to in between do we capture morphology"
    - "opennmt open source toolkit for neural machine translation"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"

Connected through hyperedge HE-3 (induced by source paper "what do deep networks like to read"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "analysis methods in neural language processing a survey"
    - "what do deep networks like to see"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-4 (induced by source paper "rethinking cooperative rationalization introspective extraction and complement control"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "game theoretic interpretability for temporal modeling"
    - "towards robust interpretability with self explaining neural networks"
    - "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "structured pruning of large language models"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "roberta a robustly optimized bert pretraining approach"
    - "the state of sparsity in deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "transformer to cnn label scarce distillation for efficient text classification"

Connected through hyperedge HE-6 (induced by source paper "educe explaining model decisions through unsupervised concepts extraction"):
  with members:
    - "xoc explainable observer classifier for explainable binary decisions"
    - "counterfactual visual explanations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "axiomatic attribution for deep networks"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "exploring hierarchical interaction between review and summary for better sentiment analysis"):
  with members:
    - "attention is not explanation"
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "rationale augmented convolutional neural networks for text classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "interpretability with accurate small models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a unified approach to interpreting model predictions"
    - "the promise and peril of human evaluation for model interpretability"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Question: Please tell me which class the center node 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)