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: "magnitude a fast efficient universal vector embedding utility package"
  Abstract: Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications. We present Magnitude, a fast, lightweight tool for utilizing and processing embeddings. Magnitude is an open source Python package with a compact vector storage file format that allows for efficient manipulation of huge numbers of embeddings. Magnitude performs common operations up to 60 to 6,000 times faster than Gensim. Magnitude introduces several novel features for improved robustness like out-of-vocabulary lookups.

Connected through hyperedge HE-1 (induced by source paper "multimodal deep networks for text and image based document classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep contextualized word representations"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "mimicking word embeddings using subword rnns"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
    - "enriching word vectors with subword information"
    - "xception deep learning with depthwise separable convolutions"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "message passing for complex question answering over knowledge graphs"):
  with members:
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "embedding logical queries on knowledge graphs"
    - "relational inductive biases deep learning and graph networks"
    - "learning to rank query graphs for complex question answering over knowledge graphs"
    - "no one is perfect analysing the performance of question answering components over the dbpedia knowledge graph"
    - "simplequestions nearly solved a new upperbound and baseline approach"

Connected through hyperedge HE-3 (induced by source paper "an improved neural baseline for temporal relation extraction"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a structured learning approach to temporal relation extraction"
    - "cogcomptime a tool for understanding time in natural language text"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multi axis annotation scheme for event temporal relations"
    - "temporal information extraction by predicting relative time lines"
    - "improving temporal relation extraction with a globally acquired statistical resource"

Connected through hyperedge HE-4 (induced by source paper "tag based semantic features for scene image classification"):
  with members:
    - "unsupervised deep features for privacy image classification"
    - "indoor image representation by high level semantic features"
    - "locally supervised deep hybrid model for scene recognition"
    - "places an image database for deep scene understanding"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "enriching word vectors with subword information"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"

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)