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: "deeper deep entity resolution"
  Abstract: Entity resolution (ER) is a key data integration problem. Despite the efforts in 70+ years in all aspects of ER, there is still a high demand for democratizing ER - humans are heavily involved in labeling data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representation of words (a.k.a. word embeddings), we present a novel ER system, called DeepER, that achieves good accuracy, high efficiency, as well as ease-of-use (i.e., much less human efforts). For accuracy, we use sophisticated composition methods, namely uni- and bi-directional recurrent neural networks (RNNs) with long short term memory (LSTM) hidden units, to convert each tuple to a distributed representation (i.e., a vector), which can in turn be used to effectively capture similarities between tuples. We consider both the case where pre-trained word embeddings are available as well the case where they are not; we present ways to learn and tune the distributed representations. For efficiency, we propose a locality sensitive hashing (LSH) based blocking approach that uses distributed representations of tuples; it takes all attributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that consider only a few attributes. For ease-of-use, DeepER requires much less human labeled data and does not need feature engineering, compared with traditional machine learning based approaches which require handcrafted features, and similarity functions along with their associated thresholds. We evaluate our algorithms on multiple datasets (including benchmarks, biomedical data, as well as multi-lingual data) and the extensive experimental results show that DeepER outperforms existing solutions.

Connected through hyperedge HE-1 (induced by source paper "autoblock a hands off blocking framework for entity matching"):
  with members:
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "practical and optimal lsh for angular distance"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "optimal data dependent hashing for approximate near neighbors"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "column2vec structural understanding via distributed representations of database schemas"):
  with members:
    - "data curation with deep learning vision towards self driving data curation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "vector embedding of wikipedia concepts and entities"
    - "graph2vec learning distributed representations of graphs"
    - "generative topic embedding a continuous representation of documents extended version with proofs"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "data curation with deep learning vision towards self driving data curation"):
  with members:
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"
    - "a survey of methods for explaining black box models"
    - "mida multiple imputation using denoising autoencoders"
    - "unsupervised string transformation learning for entity consolidation"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "data curation with deep learning vision"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "a survey on network embedding"
    - "graph2vec learning distributed representations of graphs"
    - "cross modal common representation learning by hybrid transfer network"

Connected through hyperedge HE-5 (induced by source paper "database meets deep learning challenges and opportunities"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "the case for learned index structures"
    - "an encoder decoder framework translating natural language to database queries"
    - "device placement optimization with reinforcement learning"
    - "memory efficient implementation of densenets"
    - "learning a neural semantic parser from user feedback"
    - "revisiting distributed synchronous sgd"

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.DB(Databases)