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: "bi directional block self attention for fast and memory efficient sequence modeling"
  Abstract: Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some tasks. SAN can model both such dependencies via highly parallelizable computation, but memory requirement grows rapidly in line with sequence length. In this paper, we propose a model, called "bi-directional block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding. It requires as little memory as RNN but with all the merits of SAN. Bi-BloSAN splits the entire sequence into blocks, and applies an intra-block SAN to each block for modeling local context, then applies an inter-block SAN to the outputs for all blocks to capture long-range dependency. Thus, each SAN only needs to process a short sequence, and only a small amount of memory is required. Additionally, we use feature-level attention to handle the variation of contexts around the same word, and use forward/backward masks to encode temporal order information. On nine benchmark datasets for different NLP tasks, Bi-BloSAN achieves or improves upon state-of-the-art accuracy, and shows better efficiency-memory trade-off than existing RNN/CNN/SAN.

Connected through hyperedge HE-1 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search"):
  with members:
    - "q8bert quantized 8bit bert"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "extreme language model compression with optimal subwords and shared projections"
    - "bert rediscovers the classical nlp pipeline"
    - "q bert hessian based ultra low precision quantization of bert"
    - "structured pruning of large language models"

Connected through hyperedge HE-2 (induced by source paper "conditional self attention for query based summarization"):
  with members:
    - "universal transformers"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "enhancing sentence embedding with generalized pooling"
    - "reinforced mnemonic reader for machine comprehension"
    - "neural natural language inference models enhanced with external knowledge"

Connected through hyperedge HE-3 (induced by source paper "sparse sinkhorn attention"):
  with members:
    - "the evolved transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "generating long sequences with sparse transformers"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "mesh tensorflow deep learning for supercomputers"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-4 (induced by source paper "label guided learning for text classification"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "joint embedding of words and labels for text classification"
    - "gile a generalized input label embedding for text classification"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "semi supervised classification with graph convolutional networks"
    - "recurrent neural network for text classification with multi task learning"
    - "bag of tricks for efficient text classification"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-5 (induced by source paper "albert a lite bert for self supervised learning of language representations"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "spanbert improving pre training by representing and predicting spans"
    - "well read students learn better on the importance of pre training compact models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "structbert incorporating language structures into pre training for deep language understanding"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-6 (induced by source paper "pay less attention with lightweight and dynamic convolutions"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "smoothed dilated convolutions for improved dense prediction"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "fastfusionnet new state of the art for dawnbench squad"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "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"
    - "tensorized self attention efficiently modeling pairwise and global dependencies together"

Connected through hyperedge HE-8 (induced by source paper "improving multi head attention with capsule networks"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "information aggregation for multi head attention with routing by agreement"
    - "weighted transformer network for machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "investigating capsule networks with dynamic routing for text classification"
    - "self attention with relative position representations"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "scaling neural machine translation"

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)