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: "incremental knowledge base construction using deepdive"
  Abstract: Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent names used for this problem include dealing with dark data and knowledge base construction (KBC). In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based respectively on sampling and variational techniques. We also study the tradeoff space of these methods and develop a simple rule-based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality.

Connected through hyperedge HE-1 (induced by source paper "joint reasoning for multi faceted commonsense knowledge"):
  with members:
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "embedding uncertain knowledge graphs"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "engaging image captioning via personality"
    - "moving beyond the turing test with the allen ai science challenge"
    - "vqa visual question answering"

Connected through hyperedge HE-2 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-3 (induced by source paper "scene graph prediction with limited labels"):
  with members:
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "large scale visual relationship understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "image generation from scene graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "inferring and executing programs for visual reasoning"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-4 (induced by source paper "weight annotation in information extraction"):
  with members:
    - "efficient logspace classes for enumeration counting and uniform generation"
    - "complexity bounds for relational algebra over document spanners"
    - "representing formal languages a comparison between finite automata and recurrent neural networks"
    - "constant delay enumeration for nondeterministic document spanners"
    - "constant delay algorithms for regular document spanners"
    - "document spanners for extracting incomplete information expressiveness and complexity"
    - "snorkel rapid training data creation with weak supervision"
    - "extracting automata from recurrent neural networks using queries and counterexamples"

Connected through hyperedge HE-5 (induced by source paper "overton a data system for monitoring and improving machine learned products"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "slice based learning a programming model for residual learning in critical data slices"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-6 (induced by source paper "approximate inference in structured instances with noisy categorical observations"):
  with members:
    - "holodetect few shot learning for error detection"
    - "a formal framework for probabilistic unclean databases"
    - "inference in sparse graphs with pairwise measurements and side information"
    - "holoclean holistic data repairs with probabilistic inference"
    - "harnessing deep neural networks with logic rules"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "bidirectional lstm crf models for sequence tagging"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-7 (induced by source paper "learning tractable probabilistic models in open worlds"):
  with members:
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "a differential approach to inference in bayesian networks"
    - "programming with personalized pagerank a locally groundable first order probabilistic logic"
    - "sum product networks a new deep architecture"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-8 (induced by source paper "automatic weighted matching rectifying rule discovery for data repairing"):
  with members:
    - "holoclean holistic data repairs with probabilistic inference"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

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)