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: "data curation with deep learning vision towards self driving data curation"
  Abstract: Past. Data curation - the process of discovering, integrating, and cleaning data - is one of the oldest data management problems. Unfortunately, it is still the most time consuming and least enjoyable work of data scientists. So far, successful data curation stories are mainly ad-hoc solutions that are either domain-specific (for example, ETL rules) or task-specific (for example, entity resolution). #R##N#Present. The power of current data curation solutions are not keeping up with the ever changing data ecosystem in terms of volume, velocity, variety and veracity, mainly due to the high human cost, instead of machine cost, needed for providing the ad-hoc solutions mentioned above. Meanwhile, deep learning is making strides in achieving remarkable successes in areas such as image recognition, natural language processing, and speech recognition. This is largely due to its ability to understanding features that are neither domain-specific nor task-specific. #R##N#Future. Data curation solutions need to keep the pace with the fast-changing data ecosystem, where the main hope is to devise domain-agnostic and task-agnostic solutions. To this end, we start a new research project, called AutoDC, to unleash the potential of deep learning towards self-driving data curation. We will discuss how different deep learning concepts can be adapted and extended to solve various data curation problems. We showcase some low-hanging fruits about the early encounters between deep learning and data curation happening in AutoDC. We believe that the directions pointed out by this work will not only drive AutoDC towards democratizing data curation, but also serve as a cornerstone for researchers and practitioners to move to a new realm of data curation solutions.

Connected through hyperedge HE-1 (induced by source paper "slide in defense of smart algorithms over hardware acceleration for large scale deep learning systems"):
  with members:
    - "lsh sampling breaks the computation chicken and egg loop in adaptive stochastic gradient estimation"
    - "auto tuning tensorflow threading model for cpu backend"
    - "scaling up split merge mcmc with locality sensitive sampling lss"
    - "in datacenter performance analysis of a tensor processing unit"
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "adaptive sampled softmax with kernel based sampling"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "database meets deep learning challenges and opportunities"

Connected through hyperedge HE-2 (induced by source paper "column2vec structural understanding via distributed representations of database schemas"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeper deep entity resolution"
    - "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 "learning semantic annotations for tabular data"):
  with members:
    - "colnet embedding the semantics of web tables for column type prediction"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "local embeddings for relational data integration"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "deeper deep entity resolution"):
  with members:
    - "enriching word vectors with subword information"
    - "when are tree structures necessary for deep learning of representations"
    - "how transferable are features in deep neural networks"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"
    - "hashing for similarity search a survey"
    - "a comparison of blocking methods for record linkage"
    - "efficient estimation of word representations in vector space"

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)