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: "an iterative closest point method for unsupervised word translation"
  Abstract: Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks of generative adversarial models: sensitivity to hyper-parameters, long training time and lack of interpretability. In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods. We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment. Our simple linear method is able to achieve better or equal performance to recent state-of-the-art deep adversarial approaches and typically does a little better than the supervised baseline. Our method is also efficient, easy to parallelize and interpretable.

Connected through hyperedge HE-1 (induced by source paper "zero resource cross lingual named entity recognition"):
  with members:
    - "a finnish news corpus for named entity recognition"
    - "multilingual ner transfer for low resource languages"
    - "zero resource multilingual model transfer learning what to share"
    - "deep contextualized word representations"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "fast and accurate entity recognition with iterated dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "lost in evaluation misleading benchmarks for bilingual dictionary induction"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "unimorph 2 0 universal morphology"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"

Connected through hyperedge HE-3 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental models for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"

Connected through hyperedge HE-4 (induced by source paper "unsupervised hyperalignment for multilingual word embeddings"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "unsupervised multilingual word embeddings"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-5 (induced by source paper "learning unsupervised word mapping by maximizing mean discrepancy"):
  with members:
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised cross lingual transfer of word embedding spaces"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised word mapping using structural similarities in monolingual embeddings"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "unsupervised alignment of embeddings with wasserstein procrustes"):
  with members:
    - "non adversarial unsupervised word translation"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "improving zero shot learning by mitigating the hubness problem"
    - "graph matching relax or not"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-7 (induced by source paper "loss in translation learning bilingual word mapping with a retrieval criterion"):
  with members:
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "advances in pre training distributed word representations"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "improving zero shot learning by mitigating the hubness problem"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

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.LG(Machine Learning)