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: "reasoning about linguistic regularities in word embeddings using matrix manifolds"
  Abstract: Recent work has explored methods for learning continuous vector space word representations reflecting the underlying semantics of words. Simple vector space arithmetic using cosine distances has been shown to capture certain types of analogies, such as reasoning about plurals from singulars, past tense from present tense, etc. In this paper, we introduce a new approach to capture analogies in continuous word representations, based on modeling not just individual word vectors, but rather the subspaces spanned by groups of words. We exploit the property that the set of subspaces in n-dimensional Euclidean space form a curved manifold space called the Grassmannian, a quotient subgroup of the Lie group of rotations in n- dimensions. Based on this mathematical model, we develop a modified cosine distance model based on geodesic kernels that captures relation-specific distances across word categories. Our experiments on analogy tasks show that our approach performs significantly better than the previous approaches for the given task.

Connected through hyperedge HE-1 (induced by source paper "calculated attributes of synonym sets"):
  with members:
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "multi task zero shot action recognition with prioritised data augmentation"):
  with members:
    - "dynamic concept composition for zero example event detection"
    - "transductive multi view zero shot learning"
    - "a robust and efficient video representation for action recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "semantic embedding space for zero shot action recognition"
    - "zero shot action recognition by word vector embedding"
    - "transductive zero shot action recognition by word vector embedding"
    - "improving zero shot learning by mitigating the hubness problem"

Connected through hyperedge HE-3 (induced by source paper "compressing word embeddings"):
  with members:
    - "adam a method for stochastic optimization"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "efficient estimation of word representations in vector space"

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)