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: "reuse and adaptation for entity resolution through transfer learning"
  Abstract: Entity resolution (ER) is one of the fundamental problems in data integration, where machine learning (ML) based classifiers often provide the state-of-the-art results. Considerable human effort goes into feature engineering and training data creation. In this paper, we investigate a new problem: Given a dataset D_T for ER with limited or no training data, is it possible to train a good ML classifier on D_T by reusing and adapting the training data of dataset D_S from same or related domain? Our major contributions include (1) a distributed representation based approach to encode each tuple from diverse datasets into a standard feature space; (2) identification of common scenarios where the reuse of training data can be beneficial; and (3) five algorithms for handling each of the aforementioned scenarios. We have performed comprehensive experiments on 12 datasets from 5 different domains (publications, movies, songs, restaurants, and books). Our experiments show that our algorithms provide significant benefits such as providing superior performance for a fixed training data size.

Connected through hyperedge HE-1 (induced by source paper "end to end entity resolution for big data a survey"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "a survey of blocking and filtering techniques for entity resolution"
    - "minoaner schema agnostic non iterative massively parallel resolution of web entities"
    - "pigeonring a principle for faster thresholded similarity search"
    - "snorkel rapid training data creation with weak supervision"
    - "distributed holistic clustering on linked data"
    - "a dnf blocking scheme learner for heterogeneous datasets"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "a survey of blocking and filtering techniques for entity resolution"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "scalable and robust set similarity join"
    - "set similarity search beyond minhash"
    - "a dnf blocking scheme learner for heterogeneous datasets"
    - "hashing for similarity search a survey"
    - "a comparison of blocking methods for record linkage"
    - "distributed representations of words and phrases and their compositionality"
    - "scaling multiple source entity resolution using statistically efficient transfer learning"

Connected through hyperedge HE-3 (induced by source paper "low resource deep entity resolution with transfer and active learning"):
  with members:
    - "deep contextualized word representations"
    - "cost effective active learning for deep image classification"
    - "enriching word vectors with subword information"
    - "cross domain entity resolution in social media"
    - "training very deep networks"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-4 (induced by source paper "autoer automated entity resolution using generative modelling"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models"
    - "crowder crowdsourcing entity resolution"
    - "finding connected components on map reduce in logarithmic rounds"
    - "projected subgradient methods for learning sparse gaussians"

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)