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: "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
  Abstract: Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in natural language processing (NLP). This tutorial gives an overview of the technique. We describe example algorithms, describe formal guarantees for the method, and describe practical issues in implementing the algorithms. While our examples are predominantly drawn from the NLP literature, the material should be of general relevance to inference problems in machine learning. A central theme of this tutorial is that Lagrangian relaxation is naturally applied in conjunction with a broad class of combinatorial algorithms, allowing inference in models that go significantly beyond previous work on Lagrangian relaxation for inference in graphical models.

Connected through hyperedge HE-1 (induced by source paper "mitigating gender bias in natural language processing literature review"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "model cards for model reporting"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "women also snowboard overcoming bias in captioning models"

Connected through hyperedge HE-2 (induced by source paper "target language aware constrained inference for cross lingual dependency parsing"):
  with members:
    - "how multilingual is multilingual bert"
    - "low resource syntactic transfer with unsupervised source reordering"
    - "dependency grammar induction with a neural variational transition based parser"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "word translation without parallel data"

Connected through hyperedge HE-3 (induced by source paper "mixed variable bayesian optimization"):
  with members:
    - "high dimensional bayesian optimization via additive models with overlapping groups"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "hyperparameter optimization a spectral approach"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "importance weighted autoencoders"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies"):
  with members:
    - "optimizing for measure of performance in max margin parsing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a compact linear programming relaxation for binary sub modular mrf"
    - "tighter linear program relaxations for high order graphical models"
    - "discriminative probabilistic models for relational data"
    - "fast exact inference for recursive cardinality models"
    - "tightening lp relaxations for map using message passing"
    - "complexity of inference in graphical models"

Connected through hyperedge HE-5 (induced by source paper "embedded state latent conditional random fields for sequence labeling"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "neural architectures for named entity recognition"
    - "structured prediction energy networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning soft linear constraints with application to citation field extraction"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "gradient based inference for networks with output constraints"):
  with members:
    - "a span selection model for semantic role labeling"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "towards semi supervised learning for deep semantic role labeling"
    - "sequence to sequence learning as beam search optimization"
    - "recurrent neural network grammars"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "structured prediction energy networks"

Connected through hyperedge HE-7 (induced by source paper "men also like shopping reducing gender bias amplification using corpus level constraints"):
  with members:
    - "semantics derived automatically from language corpora contain human like biases"
    - "equality of opportunity in supervised learning"
    - "commonly uncommon semantic sparsity in situation recognition"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "vqa visual question answering"
    - "a survey on measuring indirect discrimination in machine learning"
    - "microsoft coco captions data collection and evaluation server"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "prior aware dual decomposition document specific topic inference for spectral topic models"):
  with members:
    - "provable algorithms for inference in topic models"
    - "spectral methods for correlated topic models"
    - "robust spectral inference for joint stochastic matrix factorization"
    - "probabilistic latent semantic analysis"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "on smoothing and inference for topic models"
    - "a spectral algorithm for latent dirichlet allocation"

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)