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: "semantic modelling with long short term memory for information retrieval"
  Abstract: In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task.

Connected through hyperedge HE-1 (induced by source paper "model compression with multi task knowledge distillation for web scale question answering system"):
  with members:
    - "model compression via distillation and quantization"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "image to image translation for domain adaptation"
    - "channel pruning for accelerating very deep neural networks"
    - "deep identity aware transfer of facial attributes"

Connected through hyperedge HE-2 (induced by source paper "predicting evacuation decisions using representations of individuals pre disaster web search behavior"):
  with members:
    - "cross comparative analysis of evacuation behavior after earthquakes using mobile phone data"
    - "fasttext zip compressing text classification models"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "conversational help for task completion and feature discovery in personal assistants"):
  with members:
    - "sesa supervised explicit semantic analysis"
    - "improved semantic representations from tree structured long short term memory networks"
    - "a c lstm neural network for text classification"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"
    - "a personalized system for conversational recommendations"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for email search ranking with auxiliary query clustering"):
  with members:
    - "neural ranking models with weak supervision"
    - "neural models for information retrieval"
    - "wide deep learning for recommender systems"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "improved semantic representations from tree structured long short term memory networks"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-5 (induced by source paper "match tensor a deep relevance model for search"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "learning to match using local and distributed representations of text for web search"
    - "text matching as image recognition"
    - "adaptability of neural networks on varying granularity ir tasks"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-6 (induced by source paper "amobee at semeval 2017 task 4 deep learning system for sentiment detection on twitter"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "getting started with neural models for semantic matching in web search"):
  with members:
    - "a deep relevance matching model for ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "unsupervised efficient and semantic expertise retrieval"
    - "learning latent vector spaces for product search"
    - "learning distributed representations of sentences from unlabelled data"
    - "learning dynamic classes of events using stacked multilayer perceptron networks"
    - "deep learning relevance creating relevant information as opposed to retrieving it"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-8 (induced by source paper "adaptability of neural networks on varying granularity ir tasks"):
  with members:
    - "attentive pooling networks"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "applying deep learning to answer selection a study and an open task"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "convolutional neural network architectures for matching natural language sentences"
    - "lstm based deep learning models for non factoid answer selection"
    - "distributed representations of sentences and documents"

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)