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: "pair2vec compositional word pair embeddings for cross sentence inference"
  Abstract: Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This paper proposes new methods for learning and using embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are computed as a compositional function of each word's representation, which is learned by maximizing the pointwise mutual information (PMI) with the contexts in which the the two words co-occur. We add these representations to the cross-sentence attention layer of existing inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or replacing existing word embeddings. Experiments show a gain of 2.72% on the recently released SQuAD 2.0 and 1.3% on MultiNLI. Our representations also aid in better generalization with gains of around 6-7% on adversarial SQuAD datasets, and 8.8% on the adversarial entailment test set by Glockner et al.

Connected through hyperedge HE-1 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification"):
  with members:
    - "relational word embeddings"
    - "kg bert bert for knowledge graph completion"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "albert a lite bert for self supervised learning of language representations"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"

Connected through hyperedge HE-2 (induced by source paper "compositional embeddings for multi label one shot learning"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "learning feed forward one shot learners"
    - "deepsetnet predicting sets with deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "relation module for non answerable prediction on question answering"):
  with members:
    - "densely connected attention propagation for reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "compositional attention networks for machine reasoning"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "composition of sentence embeddings lessons from statistical relational learning"):
  with members:
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "evaluating compositionality in sentence embeddings"
    - "natural language inference over interaction space"
    - "evaluating vector space models of analogy"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"

Connected through hyperedge HE-5 (induced by source paper "inducing relational knowledge from bert"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "relational word embeddings"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "do neural language representations learn physical commonsense"

Connected through hyperedge HE-6 (induced by source paper "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-7 (induced by source paper "improving generalization by incorporating coverage in natural language inference"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "learning and evaluating general linguistic intelligence"
    - "multi task deep neural networks for natural language understanding"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "the natural language decathlon multitask learning as question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"

Connected through hyperedge HE-8 (induced by source paper "generating token level explanations for natural language inference"):
  with members:
    - "attention is not explanation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "e snli natural language inference with natural language explanations"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a decomposable attention model for natural language inference"

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)