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: "a formal framework for probabilistic unclean databases"
  Abstract: Traditional modeling of inconsistency in database theory casts all possible "repairs" equally likely. Yet, effective data cleaning needs to incorporate statistical reasoning. For example, yearly salary of \$100k and age of 22 are more likely than \$100k and 122 and two people with same address are likely to share their last name (i.e., a functional dependency tends to hold but may occasionally be violated). We propose a formal framework for unclean databases, where two types of statistical knowledge are incorporated. The first represents a belief of how intended (clean) data is generated, and the second represents a belief of how the actual database is realized through the introduction of noise. #R##N#Formally, a Probabilistic Unclean Database (PUD) is a triple that consists of a probabilistic database that we call the "intention", a probabilistic data transformator that we call the "realization", and a dirty database that we call the "observation". We define three computational problems in this framework: cleaning (find the most likely intention), probabilistic query answering (compute the probability of an answer tuple), and learning (find the most likely parameters given examples of clean and dirty databases). We illustrate the framework on concrete representations of PUDs, show that they generalize traditional concepts of repairs such as cardinality and value repairs, draw connection to consistent query answering, and prove tractability results. We further show that parameters can be learned in practical instantiations, and in fact, prove that under certain conditions we can learn directly from a single dirty database without any need for clean examples.

Connected through hyperedge HE-1 (induced by source paper "complexity and efficient algorithms for data inconsistency evaluating and repairing"):
  with members:
    - "computing optimal repairs for functional dependencies"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
    - "minimal change integrity maintenance using tuple deletions"

Connected through hyperedge HE-2 (induced by source paper "holodetect few shot learning for error detection"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "on calibration of modern neural networks"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "wide deep learning for recommender systems"
    - "understanding deep learning requires rethinking generalization"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "approximate inference in structured instances with noisy categorical observations"):
  with members:
    - "holodetect few shot learning for error detection"
    - "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"
    - "incremental knowledge base construction using deepdive"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-4 (induced by source paper "learning functional dependencies with sparse regression"):
  with members:
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "discovering reliable approximate functional dependencies"
    - "holoclean holistic data repairs with probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "toward a view based data cleaning architecture"):
  with members:
    - "holodetect few shot learning for error detection"
    - "minimal change integrity maintenance using tuple deletions"

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)