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: "the fine line between linguistic generalization and failure in seq2seq attention models"
  Abstract: Seq2Seq based neural architectures have become the go-to architecture to apply to sequence to sequence language tasks. Despite their excellent performance on these tasks, recent work has noted that these models usually do not fully capture the linguistic structure required to generalize beyond the dense sections of the data distribution \cite{ettinger2017towards}, and as such, are likely to fail on samples from the tail end of the distribution (such as inputs that are noisy \citep{belkinovnmtbreak} or of different lengths \citep{bentivoglinmtlength}). In this paper, we look at a model's ability to generalize on a simple symbol rewriting task with a clearly defined structure. We find that the model's ability to generalize this structure beyond the training distribution depends greatly on the chosen random seed, even when performance on the standard test set remains the same. This suggests that a model's ability to capture generalizable structure is highly sensitive. Moreover, this sensitivity may not be apparent when evaluating it on standard test sets.

Connected through hyperedge HE-1 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"):
  with members:
    - "assessing bert s syntactic abilities"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "assessing composition in sentence vector representations"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "teaching syntax by adversarial distraction"

Connected through hyperedge HE-2 (induced by source paper "capacity bandwidth and compositionality in emergent language learning"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "on the interaction between supervision and self play in emergent communication"
    - "anti efficient encoding in emergent communication"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "countering language drift via visual grounding"
    - "compositional generalization through meta sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "correlating neural and symbolic representations of language"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"

Connected through hyperedge HE-4 (induced by source paper "location attention for extrapolation to longer sequences"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "learning compositionally through attentive guidance"
    - "extrapolation in nlp"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "generalisation in humans and deep neural networks"
    - "deep learning a critical appraisal"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "using priming to uncover the organization of syntactic representations in neural language models"):
  with members:
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "the emergence of number and syntax units in lstm language models"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "a neural model of adaptation in reading"
    - "what do rnn language models learn about filler gap dependencies"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "analysis methods in neural language processing a survey"

Connected through hyperedge HE-6 (induced by source paper "white to black efficient distillation of black box adversarial attacks"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "shielding google s language toxicity model against adversarial attacks"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "synthetic and natural noise both break neural machine translation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-7 (induced by source paper "learning compositionally through attentive guidance"):
  with members:
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "compositional attention networks for machine reasoning"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "making neural programming architectures generalize via recursion"
    - "exploring human like attention supervision in visual question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "modular multitask reinforcement learning with policy sketches"

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)