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: "learning to rank question answer pairs with holographic dual lstm architecture"
  Abstract: We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As opposed to the neural tensor layer that has been adopted recently, the holographic composition provides the benefits of scalable and rich representational learning approach without incurring huge parameter costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified architecture for both deep sentence modeling and semantic matching. Essentially, our model is trained end-to-end whereby the parameters of the LSTM are optimized in a way that best explains the correlation between question and answer representations. In addition, our proposed deep learning architecture requires no extensive feature engineering. Via extensive experiments, we show that HD-LSTM outperforms many other neural architectures on two popular benchmark QA datasets. Empirical studies confirm the effectiveness of holographic composition over the neural tensor layer.

Connected through hyperedge HE-1 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "bertsel answer selection with pre trained models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "sanity check a strong alignment and information retrieval baseline for question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "bilateral multi perspective matching for natural language sentences"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-2 (induced by source paper "sentencemim a latent variable language model"):
  with members:
    - "implicit deep latent variable models for text generation"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "dynamic evaluation of transformer language models"
    - "preventing posterior collapse with delta vaes"
    - "a stable variational autoencoder for text modelling"
    - "attention is all you need"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "review guided helpful answer identification in e commerce"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "joint learning of answer selection and answer summary generation in community question answering"
    - "learning semantic textual similarity from conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "attentive pooling networks"

Connected through hyperedge HE-4 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-5 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks"):
  with members:
    - "quaternion collaborative filtering for recommendation"
    - "quaternion convolutional neural networks"
    - "universal transformers"
    - "quaternion knowledge graph embedding"
    - "attending to mathematical language with transformers"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "gated group self attention for answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "self attentional acoustic models"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "non local neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-7 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "zero shot user intent detection via capsule neural networks"
    - "towards linear time neural machine translation with capsule networks"
    - "dynamic routing between capsules"
    - "attentive pooling networks"
    - "simple question answering by attentive convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "hashing based answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"
    - "attention is all you need"
    - "supervised and unsupervised transfer learning for question answering"
    - "neural domain adaptation for biomedical question answering"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "hashnet deep learning to hash by continuation"

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.IR(Information Retrieval)