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: "evaluating the ability of lstms to learn context free grammars"
  Abstract: While long short-term memory (LSTM) neural net architectures are designed to capture sequence information, human language is generally composed of hierarchical structures. This raises the question as to whether LSTMs can learn hierarchical structures. We explore this question with a well-formed bracket prediction task using two types of brackets modeled by an LSTM. Demonstrating that such a system is learnable by an LSTM is the first step in demonstrating that the entire class of CFLs is also learnable. We observe that the model requires exponential memory in terms of the number of characters and embedded depth, where a sub-linear memory should suffice. Still, the model does more than memorize the training input. It learns how to distinguish between relevant and irrelevant information. On the other hand, we also observe that the model does not generalize well. We conclude that LSTMs do not learn the relevant underlying context-free rules, suggesting the good overall performance is attained rather by an efficient way of evaluating nuisance variables. LSTMs are a way to quickly reach good results for many natural language tasks, but to understand and generate natural language one has to investigate other concepts that can make more direct use of natural language's structural nature.

Connected through hyperedge HE-1 (induced by source paper "assessing the memory ability of recurrent neural networks"):
  with members:
    - "learning with interpretable structure from rnn"
    - "exploring the syntactic abilities of rnns with multi task learning"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "language modeling with gated convolutional networks"
    - "using the output embedding to improve language models"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "character aware neural language models"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-2 (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"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"

Connected through hyperedge HE-3 (induced by source paper "theoretical limitations of self attention in neural sequence models"):
  with members:
    - "assessing the ability of self attention networks to learn word order"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "bert rediscovers the classical nlp pipeline"
    - "on the computational power of rnns"
    - "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"
    - "open sesame getting inside bert s linguistic knowledge"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-4 (induced by source paper "correlating neural and symbolic representations of language"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "deep contextualized word representations"
    - "how agents see things on visual representations in an emergent language game"
    - "analysis methods in neural language processing a survey"
    - "understanding learning dynamics of language models with svcca"
    - "limitations in learning an interpreted language with recurrent models"

Connected through hyperedge HE-5 (induced by source paper "the neural state pushdown automata"):
  with members:
    - "on the computational power of rnns"
    - "learning by abstraction the neural state machine"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "context free transductions with neural stacks"
    - "number sequence prediction problems for evaluating computational powers of neural networks"
    - "biologically motivated algorithms for propagating local target representations"
    - "unbiased online recurrent optimization"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-6 (induced by source paper "lstm networks can perform dynamic counting"):
  with members:
    - "context free transductions with neural stacks"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "learning operations on a stack with neural turing machines"
    - "learning to transduce with unbounded memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "on the computational power of rnns"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "recurrent neural networks as weighted language recognizers"
    - "named entity recognition with bidirectional lstm cnns"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"

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)