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: "incremental skip gram model with negative sampling"
  Abstract: This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives. Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update. To address this problem, we present a simple incremental extension of SGNS and provide a thorough theoretical analysis to demonstrate its validity. Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm.

Connected through hyperedge HE-1 (induced by source paper "capturing evolution in word usage just add more clusters"):
  with members:
    - "towards better substitution based word sense induction"
    - "time out temporal referencing for robust modeling of lexical semantic change"
    - "does bert make any sense interpretable word sense disambiguation with contextualized embeddings"
    - "simple dynamic word embeddings for mapping perceptions in the public sphere"
    - "leveraging contextual embeddings for detecting diachronic semantic shift"
    - "survey of computational approaches to diachronic conceptual change"
    - "a state of the art of semantic change computation"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "temporal network representation learning"):
  with members:
    - "heterogeneous network motifs"
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "models for capturing temporal smoothness in evolving networks for learning latent representation of nodes"
    - "deep generative dual memory network for continual learning"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "psdvec a toolbox for incremental and scalable word embedding"

Connected through hyperedge HE-3 (induced by source paper "leveraging contextual embeddings for detecting diachronic semantic shift"):
  with members:
    - "short term meaning shift a distributional exploration"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diachronic word embeddings and semantic shifts a survey"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"

Connected through hyperedge HE-4 (induced by source paper "diachronic word embeddings and semantic shifts a survey"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "learning word relatedness over time"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "statistically significant detection of linguistic change"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-5 (induced by source paper "dynnode2vec scalable dynamic network embedding"):
  with members:
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "streaming word embeddings with the space saving algorithm"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "temporal analysis of language through neural language models"
    - "node classification in social networks"

Connected through hyperedge HE-6 (induced by source paper "sketching linear classifiers over data streams"):
  with members:
    - "introduction to online convex optimization"
    - "algorithmic decision making and the cost of fairness"
    - "federated multi task learning"
    - "macrobase prioritizing attention in fast data"
    - "why should i trust you explaining the predictions of any classifier"
    - "heavy hitters via cluster preserving clustering"
    - "federated optimization distributed optimization beyond the datacenter"
    - "theory of dual sparse regularized randomized reduction"

Connected through hyperedge HE-7 (induced by source paper "temporal dynamics of semantic relations in word embeddings an application to predicting armed conflict participants"):
  with members:
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "clustering comparable corpora of russian and ukrainian academic texts word embeddings and semantic fingerprints"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-8 (induced by source paper "streaming word embeddings with the space saving algorithm"):
  with members:
    - "compositional morphology for word representations and language modelling"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"
    - "natural language processing almost from scratch"

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)