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: "on evaluating the generalization of lstm models in formal languages"
  Abstract: Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing. Yet, there still remains an uncertainty regarding their language learning capabilities. In this paper, we empirically evaluate the inductive learning capabilities of Long Short-Term Memory networks, a popular extension of simple RNNs, to learn simple formal languages, in particular $a^nb^n$, $a^nb^nc^n$, and $a^nb^nc^nd^n$. We investigate the influence of various aspects of learning, such as training data regimes and model capacity, on the generalization to unobserved samples. We find striking differences in model performances under different training settings and highlight the need for careful analysis and assessment when making claims about the learning capabilities of neural network models.

Connected through hyperedge HE-1 (induced by source paper "neural arithmetic units"):
  with members:
    - "learning dexterous in hand manipulation"
    - "neural arithmetic expression calculator"
    - "neural arithmetic logic units"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving the neural gpu architecture for algorithm learning"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "grid long short term memory"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "automatically extracting challenge sets for non local phenomena neural machine translation"):
  with members:
    - "star transformer"
    - "linguistic evaluation of german english machine translation using a test suite"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic neural machine translation using amr"
    - "semantic structural evaluation for text simplification"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"

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 "memory augmented recurrent neural networks can learn generalized dyck languages"):
  with members:
    - "theoretical limitations of self attention in neural sequence models"
    - "sequential neural networks as automata"
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "attention is all you need"
    - "lie access neural turing machine"
    - "learning operations on a stack with neural turing machines"
    - "neural random access machines"

Connected through hyperedge HE-5 (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"
    - "evaluating the ability of lstms to learn context free grammars"
    - "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-6 (induced by source paper "measuring arithmetic extrapolation performance"):
  with members:
    - "neural arithmetic logic units"
    - "improving the neural gpu architecture for algorithm learning"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "grid long short term memory"
    - "neural gpus learn algorithms"
    - "adam a method for stochastic optimization"
    - "learning to execute"

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)