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: "effective and complete discovery of order dependencies via set based axiomatization"
  Abstract: Integrity constraints (ICs) provide a valuable tool for expressing and enforcing application semantics. However, formulating constraints manually requires domain expertise, is prone to human errors, and may be excessively time consuming, especially on large datasets. Hence, proposals for automatic discovery have been made for some classes of ICs, such as functional dependencies (FDs), and recently, order dependencies (ODs). ODs properly subsume FDs, as they can additionally express business rules involving order; e.g., an employee never has a higher salary while paying lower taxes compared with another employee. #R##N#We address the limitations of prior work on OD discovery which has factorial complexity in the number of attributes, is incomplete (i.e., it does not discover valid ODs that cannot be inferred from the ones found) and is not concise (i.e., it can result in "redundant" discovery and overly large discovery sets). We improve significantly on complexity, offer completeness, and define a compact canonical form. This is based on a novel polynomial mapping to a canonical form for ODs, and a sound and complete set of axioms (inference rules) for canonical ODs. This allows us to develop an efficient set-containment, lattice-driven OD discovery algorithm that uses the inference rules to prune the search space. Our algorithm has exponential worst-case time complexity in the number of attributes and linear complexity in the number of tuples. We prove that it produces a complete, minimal set of ODs (i.e., minimal with regards to the canonical representation). Finally, using real and synthetic datasets, we experimentally show orders-of-magnitude performance improvements over the current state-of-the-art algorithm and demonstrate effectiveness of our techniques.

Connected through hyperedge HE-1 (induced by source paper "data invariants on trust in data driven systems"):
  with members:
    - "tutorial safe and reliable machine learning"
    - "example driven query intent discovery abductive reasoning using semantic similarity"
    - "data sanity check for deep learning systems via learnt assertions"
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "on calibration of modern neural networks"
    - "learning representations and generative models for 3d point clouds"
    - "on the safety of machine learning cyber physical systems decision sciences and data products"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "abc of order dependencies"):
  with members:
    - "distributed dependency discovery"
    - "fundamentals of order dependencies"

Connected through hyperedge HE-3 (induced by source paper "discovery of band order dependencies"):
  with members:
    - "errata note discovering order dependencies through order compatibility"
    - "fundamentals of order dependencies"

Connected through hyperedge HE-4 (induced by source paper "guided automated learning for query workload re optimization"):
  with members:
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"

Connected through hyperedge HE-5 (induced by source paper "errata note discovering order dependencies through order compatibility"):
  with members:
    - "fundamentals of order dependencies"

Connected through hyperedge HE-6 (induced by source paper "efficient discovery of ontology functional dependencies"):
  with members:
    - "data cleaning and query answering with matching dependencies and matching functions"
    - "discovering matching dependencies"

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)