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: "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
  Abstract: Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks. Despite their success, it still remains a challenge to understand their hidden behavior. In this work, we analyze and interpret the cumulative nature of RNN via a proposed technique named as Layer-wIse-Semantic-Accumulation (LISA) for explaining decisions and detecting the most likely (i.e., saliency) patterns that the network relies on while decision making. We demonstrate (1) LISA: "How an RNN accumulates or builds semantics during its sequential processing for a given text example and expected response" (2) Example2pattern: "How the saliency patterns look like for each category in the data according to the network in decision making". We analyse the sensitiveness of RNNs about different inputs to check the increase or decrease in prediction scores and further extract the saliency patterns learned by the network. We employ two relation classification datasets: SemEval 10 Task 8 and TAC KBP Slot Filling to explain RNN predictions via the LISA and example2pattern.

Connected through hyperedge HE-1 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "context free transductions with neural stacks"

Connected through hyperedge HE-2 (induced by source paper "lifelong neural topic learning in contextualized autoregressive topic models of language via informative transfers"):
  with members:
    - "improving topic models with latent feature word representations"
    - "variational inference in pachinko allocation machines"
    - "replicated siamese lstm in ticketing system for similarity learning and retrieval in asymmetric texts"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "document neural autoregressive distribution estimation"
    - "distributed representations of sentences and documents"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-3 (induced by source paper "neural architectures for fine grained propaganda detection in news"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "fine grained analysis of propaganda in news articles"
    - "neural relation extraction within and across sentence boundaries"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argotario computational argumentation meets serious games"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "combining recurrent and convolutional neural networks for relation classification"

Connected through hyperedge HE-4 (induced by source paper "e t rnn applying deep learning to credit loan applications"):
  with members:
    - "rnns implicitly implement tensor product representations"
    - "snapshot ensembles train 1 get m for free"
    - "behavior revealed in mobile phone usage predicts loan repayment"
    - "sgdr stochastic gradient descent with restarts"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "cyclical learning rates for training neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "linguistically informed relation extraction and neural architectures for nested named entity recognition in bionlp ost 2019"):
  with members:
    - "neural relation extraction within and across sentence boundaries"
    - "joint bootstrapping machines for high confidence relation extraction"
    - "cross sentence n ary relation extraction with graph lstms"
    - "semi supervised multitask learning for sequence labeling"
    - "a multi task approach for named entity recognition in social media data"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "bionlp ost 2019 rdoc tasks multi grain neural relevance ranking using topics and attention based query document sentence interactions"):
  with members:
    - "deep relevance ranking using enhanced document query interactions"
    - "replicated siamese lstm in ticketing system for similarity learning and retrieval in asymmetric texts"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "distributed representations of words and phrases and their compositionality"

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)