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: "database meets deep learning challenges and opportunities"
  Abstract: Deep learning has recently become very popular on account of its incredible success in many complex datadriven applications, including image classification and speech recognition. The database community has worked on data-driven applications for many years, and therefore should be playing a lead role in supporting this new wave. However, databases and deep learning are different in terms of both techniques and applications. In this paper, we discuss research problems at the intersection of the two fields. In particular, we discuss possible improvements for deep learning systems from a database perspective, and analyze database applications that may benefit from deep learning techniques.

Connected through hyperedge HE-1 (induced by source paper "facilitating sql query composition and analysis"):
  with members:
    - "query2vec nlp meets databases for generalized workload analytics"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a survey of machine learning for big code and naturalness"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "convolutional neural networks for text categorization shallow word level vs deep character level"
    - "very deep convolutional networks for text classification"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "locec local community based edge classification in large online social networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "model slicing for supporting complex analytics with elastic inference cost and resource constraints"
    - "real time influence maximization on dynamic social streams"
    - "revenue maximization in incentivized social advertising"
    - "xgboost a scalable tree boosting system"
    - "fast r cnn"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "social influence in social advertising evidence from field experiments"

Connected through hyperedge HE-3 (induced by source paper "neo a learned query optimizer"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning scheduling algorithms for data processing clusters"
    - "learning to optimize join queries with deep reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "data curation with deep learning vision towards self driving data curation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an empirical analysis of deep learning for cardinality estimation"):
  with members:
    - "multi attribute selectivity estimation using deep learning"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "state representation learning for control an overview"
    - "towards a hands free query optimizer through deep learning"
    - "pool based sequential active learning for regression"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-6 (induced by source paper "efficient memory management for gpu based deep learning systems"):
  with members:
    - "training wide residual networks for deployment using a single bit for each weight"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "memory efficient implementation of densenets"
    - "comparative study of deep learning software frameworks"
    - "lifetime based memory management for distributed data processing systems"
    - "eie efficient inference engine on compressed deep neural network"
    - "training deep nets with sublinear memory cost"

Connected through hyperedge HE-7 (induced by source paper "plan structured deep neural network models for query performance prediction"):
  with members:
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "lift reinforcement learning in computer systems by learning from demonstrations"
    - "the case for learned index structures"
    - "attention is all you need"
    - "wisedb a learning based workload management advisor for cloud databases"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "progressive neural index search for database system"):
  with members:
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "sosd a benchmark for learned indexes"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "snas stochastic neural architecture search"

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)