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: "baselines and test data for cross lingual inference"
  Abstract: The recent years have seen a revival of interest in textual entailment, sparked by i) the emergence of powerful deep neural network learners for natural language processing and ii) the timely development of large-scale evaluation datasets such as SNLI. Recast as natural language inference, the problem now amounts to detecting the relation between pairs of statements: they either contradict or entail one another, or they are mutually neutral. Current research in natural language inference is effectively exclusive to English. In this paper, we propose to advance the research in SNLI-style natural language inference toward multilingual evaluation. To that end, we provide test data for four major languages: Arabic, French, Spanish, and Russian. We experiment with a set of baselines. Our systems are based on cross-lingual word embeddings and machine translation. While our best system scores an average accuracy of just over 75%, we focus largely on enabling further research in multilingual inference.

Connected through hyperedge HE-1 (induced by source paper "xcmrc evaluating cross lingual machine reading comprehension"):
  with members:
    - "a corpus for multilingual document classification in eight languages"
    - "xnli evaluating cross lingual sentence representations"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "zero shot cross lingual classification using multilingual neural machine translation"):
  with members:
    - "deep contextualized word representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "stochastic answer networks for machine reading comprehension"
    - "semi supervised sequence tagging with bidirectional language models"
    - "what makes imagenet good for transfer learning"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"

Connected through hyperedge HE-3 (induced by source paper "learning cross lingual sentence representations via a multi task dual encoder model"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "universal sentence encoder"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-4 (induced by source paper "hyperlex a large scale evaluation of graded lexical entailment"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a decomposable attention model for natural language inference"
    - "intrinsic subspace evaluation of word embedding representations"
    - "counter fitting word vectors to linguistic constraints"
    - "problems with evaluation of word embeddings using word similarity tasks"

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)