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: "smoke fine grained lineage at interactive speed"
  Abstract: Data lineage describes the relationship between individual input and output data items of a workflow, and has served as an integral ingredient for both traditional (e.g., debugging, auditing, data integration, and security) and emergent (e.g., interactive visualizations, iterative analytics, explanations, and cleaning) applications. The core, long-standing problem that lineage systems need to address---and the main focus of this paper---is to capture the relationships between input and output data items across a workflow with the goal to streamline queries over lineage. Unfortunately, current lineage systems either incur high lineage capture overheads, or lineage query processing costs, or both. As a result, applications, that in principle can express their logic declaratively in lineage terms, resort to hand-tuned implementations. To this end, we introduce Smoke, an in-memory database engine that neither lineage capture overhead nor lineage query processing needs to be compromised. To do so, Smoke introduces tight integration of the lineage capture logic into physical database operators; efficient, write-optimized lineage representations for storage; and optimizations when future lineage queries are known up-front. Our experiments on microbenchmarks and realistic workloads show that Smoke reduces the lineage capture overhead and streamlines lineage queries by multiple orders of magnitude compared to state-of-the-art alternatives. Our experiments on real-world applications highlight that Smoke can meet the latency requirements of interactive visualizations (e.g., <150ms) and outperform hand-written implementations of data profiling primitives.

Connected through hyperedge HE-1 (induced by source paper "vamsa tracking provenance in data science scripts"):
  with members:
    - "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml"
    - "provenance for interactive visualizations"
    - "catboost unbiased boosting with categorical features"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-2 (induced by source paper "explaining wrong queries using small examples"):
  with members:
    - "semi supervised verified feedback generation"
    - "data generation for testing and grading sql queries"
    - "on the limitations of provenance for queries with difference"
    - "provenance for aggregate queries"
    - "faster query answering in probabilistic databases using read once functions"

Connected through hyperedge HE-3 (induced by source paper "diel transparent scaling for interactive visualization"):
  with members:
    - "kyrix interactive visual data exploration at scale"
    - "making sense of asynchrony in interactive data visualizations"

Connected through hyperedge HE-4 (induced by source paper "programming with timespans in interactive visualizations"):
  with members:
    - "the bigdawg architecture"

Connected through hyperedge HE-5 (induced by source paper "deeplens towards a visual data management system"):
  with members:
    - "physical representation based predicate optimization for a visual analytics database"
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "the case for automatic database administration using deep reinforcement learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "cuttlefish a lightweight primitive for adaptive query processing"
    - "noscope optimizing neural network queries over video at scale"
    - "deeper depth prediction with fully convolutional residual networks"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-6 (induced by source paper "provenance for interactive visualizations"):
  with members:
    - "seq2seq vis a visual debugging tool for sequence to sequence models"
    - "algorithms for provisioning queries and analytics"
    - "timemachine timeline generation for knowledge base entities"
    - "rapid sampling for visualizations with ordering guarantees"

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)