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: "provenance for aggregate queries"
  Abstract: We study in this paper provenance information for queries with aggregation. Provenance information was studied in the context of various query languages that do not allow for aggregation, and recent work has suggested to capture provenance by annotating the different database tuples with elements of a commutative semiring and propagating the annotations through query evaluation. We show that aggregate queries pose novel challenges rendering this approach inapplicable. Consequently, we propose a new approach, where we annotate with provenance information not just tuples but also the individual values within tuples, using provenance to describe the values computation. We realize this approach in a concrete construction, first for "simple" queries where the aggregation operator is the last one applied, and then for arbitrary (positive) relational algebra queries with aggregation; the latter queries are shown to be more challenging in this context. Finally, we use aggregation to encode queries with difference, and study the semantics obtained for such queries on provenance annotated databases.

Connected through hyperedge HE-1 (induced by source paper "priu a provenance based approach for incrementally updating regression models"):
  with members:
    - "holodetect few shot learning for error detection"
    - "manipulating and measuring model interpretability"
    - "boostclean automated error detection and repair for machine learning"
    - "the mythos of model interpretability"
    - "processing analytical workloads incrementally"
    - "linview incremental view maintenance for complex analytical queries"
    - "putting lipstick on pig enabling database style workflow provenance"

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

Connected through hyperedge HE-3 (induced by source paper "provenance analysis for logic and games"):
  with members:
    - "semiring provenance for first order model checking"
    - "on the limitations of provenance for queries with difference"

Connected through hyperedge HE-4 (induced by source paper "snapshot semantics for temporal multiset relations extended version"):
  with members:
    - "on the limitations of provenance for queries with difference"

Connected through hyperedge HE-5 (induced by source paper "explaining aggregates for exploratory analytics"):
  with members:
    - "foresight recommending visual insights"
    - "axiomatic attribution for deep networks"
    - "database learning toward a database that becomes smarter every time"
    - "macrobase prioritizing attention in fast data"
    - "macrobase analytic monitoring for the internet of things"
    - "why should i trust you explaining the predictions of any classifier"
    - "qfix diagnosing errors through query histories"
    - "perfxplain debugging mapreduce job performance"

Connected through hyperedge HE-6 (induced by source paper "invariance to ordinal transformations in rank aware databases"):
  with members:
    - "relational division in rank aware databases"
    - "sensitivity analysis for declarative relational query languages with ordinal ranks"

Connected through hyperedge HE-7 (induced by source paper "language integrated provenance"):
  with members:
    - "database queries that explain their work"
    - "effective quotation relating approaches to language integrated query"
    - "first class functions for first order database engines"
    - "provenance as dependency analysis"

Connected through hyperedge HE-8 (induced by source paper "reenactment for read committed snapshot isolation"):
  with members:
    - "first order provenance games"
    - "putting lipstick on pig enabling database style workflow provenance"
    - "a critique of ansi sql isolation levels"

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)