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: "tensorized self attention efficiently modeling pairwise and global dependencies together"
  Abstract: Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, their expressive power and performance can be boosted by using a vector to measure pairwise dependency, but this requires to expand the alignment matrix to a tensor, which results in memory and computation bottlenecks. In this paper, we propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" (MTSA), which is as fast and as memory-efficient as a CNN, but significantly outperforms previous CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token) and global (source2token) dependencies by a novel compatibility function composed of dot-product and additive attentions, 2) uses a tensor to represent the feature-wise alignment scores for better expressive power but only requires parallelizable matrix multiplications, and 3) combines multi-head with multi-dimensional attentions, and applies a distinct positional mask to each head (subspace), so the memory and computation can be distributed to multiple heads, each with sequential information encoded independently. The experiments show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or competitive performance on nine NLP benchmarks with compelling memory- and time-efficiency.

Connected through hyperedge HE-1 (induced by source paper "fastfusionnet new state of the art for dawnbench squad"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "fast directional self attention mechanism"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"

Connected through hyperedge HE-2 (induced by source paper "transferable end to end aspect based sentiment analysis with selective adversarial learning"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect term extraction with history attention and selective transformation"
    - "adaptive semi supervised learning for cross domain sentiment classification"
    - "interactive attention networks for aspect level sentiment classification"
    - "disan directional self attention network for rnn cnn free language understanding"

Connected through hyperedge HE-3 (induced by source paper "self attention enhanced selective gate with entity aware embedding for distantly supervised relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning to propagate for graph meta learning"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "ssa cnn semantic self attention cnn for pedestrian detection"
    - "combining distant and direct supervision for neural relation extraction"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "multi level structured self attentions for distantly supervised relation extraction"

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)