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: "tuffy scaling up statistical inference in markov logic networks using an rdbms"
  Abstract: Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their wide-spread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to leverage the full power of the relational optimizer, (2) a novel hybrid architecture that allows us to perform AI-style local search efficiently using an RDBMS, and (3) a theoretical insight that shows when one can (exponentially) improve the efficiency of stochastic local search. We leverage (3) to build novel partitioning, loading, and parallel algorithms. We show that our approach outperforms state-of-the-art implementations in both quality and speed on several publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "graph partition neural networks for semi supervised classification"
    - "fast generation for convolutional autoregressive models"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-2 (induced by source paper "non parametric learning of gaifman models"):
  with members:
    - "dual convolutional neural network for graph of graphs link prediction"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "discriminative gaifman models"
    - "probabilistic similarity logic"

Connected through hyperedge HE-3 (induced by source paper "estimating aggregate properties in relational networks with unobserved data"):
  with members:
    - "gmnn graph markov neural networks"
    - "graph summarization methods and applications a survey"

Connected through hyperedge HE-4 (induced by source paper "a hybrid data cleaning framework using markov logic networks"):
  with members:
    - "holoclean holistic data repairs with probabilistic inference"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "complexity and approximation of fixing numerical attributes in databases under integrity constraints"

Connected through hyperedge HE-5 (induced by source paper "automatic weighted matching rectifying rule discovery for data repairing"):
  with members:
    - "holoclean holistic data repairs with probabilistic inference"
    - "incremental knowledge base construction using deepdive"
    - "hinge loss markov random fields and probabilistic soft logic"

Connected through hyperedge HE-6 (induced by source paper "pari a probabilistic approach to as relationships inference"):
  with members:
    - "as relationships inference and validation"

Connected through hyperedge HE-7 (induced by source paper "learning to speed up query planning in graph databases"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "taming subgraph isomorphism for rdf query processing"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-8 (induced by source paper "beyond markov logic efficient mining of prediction rules in large graphs"):
  with members:
    - "knowledge base completion baselines strike back"
    - "analogical inference for multi relational embeddings"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "improving the accuracy and efficiency of map inference for markov logic"

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)