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 iit bombay english hindi parallel corpus"
  Abstract: We present the IIT Bombay English-Hindi Parallel Corpus. The corpus is a compilation of parallel corpora previously available in the public domain as well as new parallel corpora we collected. The corpus contains 1.49 million parallel segments, of which 694k segments were not previously available in the public domain. The corpus has been pre-processed for machine translation, and we report baseline phrase-based SMT and NMT translation results on this corpus. This corpus has been used in two editions of shared tasks at the Workshop on Asian Language Translation (2016 and 2017). The corpus is freely available for non-commercial research. To the best of our knowledge, this is the largest publicly available English-Hindi parallel corpus.

Connected through hyperedge HE-1 (induced by source paper "pmindia a collection of parallel corpora of languages of india"):
  with members:
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "a call for clarity in reporting bleu scores"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "a universal parent model for low resource neural machine translation transfer"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "rapid adaptation of neural machine translation to new languages"
    - "transfer learning in multilingual neural machine translation with dynamic vocabulary"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "towards automatic face to face translation"):
  with members:
    - "deep audio visual speech recognition"
    - "a baseline neural machine translation system for indian languages"
    - "massively multilingual neural machine translation"
    - "neural voice cloning with a few samples"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "rapid adaptation of neural machine translation to new languages"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"

Connected through hyperedge HE-4 (induced by source paper "unicoder a universal language encoder by pre training with multiple cross lingual tasks"):
  with members:
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"

Connected through hyperedge HE-5 (induced by source paper "cross lingual training for automatic question generation"):
  with members:
    - "cross lingual language model pretraining"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "automating reading comprehension by generating question and answer pairs"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "from english to foreign languages transferring pre trained language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "a baseline neural machine translation system for indian languages"):
  with members:
    - "massively multilingual neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "rapid adaptation of neural machine translation to new languages"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "transfer learning for low resource neural machine translation"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-8 (induced by source paper "entity projection via machine translation for cross lingual ner"):
  with members:
    - "pioner datasets and baselines for armenian named entity recognition"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "word translation without parallel data"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "polyglot ner massive multilingual named entity recognition"

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)