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: "second order word embeddings from nearest neighbor topological features"
  Abstract: We introduce second-order vector representations of words, induced from nearest neighborhood topological features in pre-trained contextual word embeddings. We then analyze the effects of using second-order embeddings as input features in two deep natural language processing models, for named entity recognition and recognizing textual entailment, as well as a linear model for paraphrase recognition. Surprisingly, we find that nearest neighbor information alone is sufficient to capture most of the performance benefits derived from using pre-trained word embeddings. Furthermore, second-order embeddings are able to handle highly heterogeneous data better than first-order representations, though at the cost of some specificity. Additionally, augmenting contextual embeddings with second-order information further improves model performance in some cases. Due to variance in the random initializations of word embeddings, utilizing nearest neighbor features from multiple first-order embedding samples can also contribute to downstream performance gains. Finally, we identify intriguing characteristics of second-order embedding spaces for further research, including much higher density and different semantic interpretations of cosine similarity.

Connected through hyperedge HE-1 (induced by source paper "characterizing the impact of geometric properties of word embeddings on task performance"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "improving negative sampling for word representation using self embedded features"
    - "insights into analogy completion from the biomedical domain"
    - "the role of context types and dimensionality in learning word embeddings"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "second order co occurrence sensitivity of skip gram with negative sampling"):
  with members:
    - "deep contextualized word representations"
    - "probabilistic fasttext for multi sense word embeddings"
    - "node2vec scalable feature learning for networks"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "choosing the word most typical in context using a lexical co occurrence network"

Connected through hyperedge HE-3 (induced by source paper "factors influencing the surprising instability of word embeddings"):
  with members:
    - "distributed representations of words and phrases and their compositionality"
    - "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.CL(Computation and Language)