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: "hierarchical embeddings for hypernymy detection and directionality"
  Abstract: We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym$-$hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state$-$of$-$the$-$art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.

Connected through hyperedge HE-1 (induced by source paper "discriminative topic mining via category name guided text embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "spherical text embedding"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"

Connected through hyperedge HE-2 (induced by source paper "using distributional thesaurus embedding for co hyponymy detection"):
  with members:
    - "network features based co hyponymy detection"
    - "can network embedding of distributional thesaurus be combined with word vectors for better representation"
    - "struc2vec learning node representations from structural identity"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "node2vec scalable feature learning for networks"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "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"
    - "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-4 (induced by source paper "informing unsupervised pretraining with external linguistic knowledge"):
  with members:
    - "towards universal dialogue state tracking"
    - "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"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "neural network acceptability judgments"
    - "attention is all you need"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-5 (induced by source paper "tifi taxonomy induction for fictional domains extended version"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "taxonomy induction using hypernym subsequences"
    - "augur mining human behaviors from fiction to power interactive systems"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "inferring interpersonal relations in narrative summaries"
    - "you re mr lebowski i m the dude inducing address term formality in signed social networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "embedding text in hyperbolic spaces"):
  with members:
    - "trimming and improving skip thought vectors"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "specialising word vectors for lexical entailment"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "speeding up context based sentence representation learning with non autoregressive convolutional decoding"
    - "hyperlex a large scale evaluation of graded lexical entailment"

Connected through hyperedge HE-7 (induced by source paper "awe asymmetric word embedding for textual entailment"):
  with members:
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "natural language inference over interaction space"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "specialising word vectors for lexical entailment"
    - "bilateral multi perspective matching for natural language sentences"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "unsupervised hypernym detection by distributional inclusion vector embedding"):
  with members:
    - "specialising word vectors for lexical entailment"
    - "poincar e embeddings for learning hierarchical representations"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "lifted rule injection for relation embeddings"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "unsupervised measure of word similarity how to outperform co occurrence and vector cosine in vsms"
    - "improving hypernymy detection with an integrated path based and distributional method"

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)