human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "single training dimension selection for word embedding with pca"):
  Abstract: In this paper, we present a fast and reliable method based on PCA to select the number of dimensions for word embeddings. First, we train one embedding with a generous upper bound (e.g. 1,000) of dimensions. Then we transform the embeddings using PCA and incrementally remove the lesser dimensions one at a time while recording the embeddings' performance on language tasks. Lastly, we select the number of dimensions while balancing model size and accuracy. Experiments using various datasets and language tasks demonstrate that we are able to train 10 times fewer sets of embeddings while retaining optimal performance. Researchers interested in training the best-performing embeddings for downstream tasks, such as sentiment analysis, question answering and hypernym extraction, as well as those interested in embedding compression should find the method helpful.

Includes members:
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "on the dimensionality of word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "nationality classification using name embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the secret lives of names name embeddings from social media")
  Member "simple and effective dimensionality reduction for word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "normalization of input output shared embeddings in text generation models")
  Member "all but the top simple and effective postprocessing for word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "compressing word embeddings via deep compositional code learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding the downstream instability of word embeddings")
  Member "how polarized have we become a multimodal classification of trump followers and clinton followers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fairface face attribute dataset for balanced race gender and age")
  Member "poincar e embeddings for learning hierarchical representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the tensor brain semantic decoding for perception and memory")

Question: Please tell me which class the source paper 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)