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: "counter fitting word vectors to linguistic constraints"
  Abstract: In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. We also show how the method can be used to tailor the word vector space for the downstream task of dialogue state tracking, resulting in robust improvements across different dialogue domains.

Connected through hyperedge HE-1 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-2 (induced by source paper "knowledge enhanced attention for robust natural language inference"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "improving question answering by commonsense based pre training"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"

Connected through hyperedge HE-3 (induced by source paper "correlations between word vector sets"):
  with members:
    - "correlation coefficients and semantic textual similarity"
    - "similarity of neural network representations revisited"
    - "model comparison for semantic grouping"
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"

Connected through hyperedge HE-4 (induced by source paper "is bert really robust a strong baseline for natural language attack on text classification and entailment"):
  with members:
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "probing neural network comprehension of natural language arguments"
    - "pathologies of neural models make interpretations difficult"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "achieving verified robustness to symbol substitutions via interval bound propagation"):
  with members:
    - "knowing when to stop evaluation and verification of conformity to output size specifications"
    - "formal security analysis of neural networks using symbolic intervals"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "training verified learners with learned verifiers"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "deep contextualized word representations"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "interactive refinement of cross lingual word embeddings"):
  with members:
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "a survey of cross lingual word embedding models"
    - "are girls neko or sh ojo cross lingual alignment of non isomorphic embeddings with iterative normalization"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "exploiting cross lingual subword similarities in low resource document classification"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-7 (induced by source paper "towards open intent discovery for conversational text"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "zero shot user intent detection via capsule neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi domain adversarial learning for slot filling in spoken language understanding"

Connected through hyperedge HE-8 (induced by source paper "certified robustness to adversarial word substitutions"):
  with members:
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "combating adversarial misspellings with robust word recognition"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "training verified learners with learned verifiers"
    - "generating natural language adversarial examples"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)