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: "scaling multiple source entity resolution using statistically efficient transfer learning"
  Abstract: We consider a serious, previously-unexplored challenge facing almost all approaches to scaling up entity resolution (ER) to multiple data sources: the prohibitive cost of labeling training data for supervised learning of similarity scores for each pair of sources. While there exists a rich literature describing almost all aspects of pairwise ER, this new challenge is arising now due to the unprecedented ability to acquire and store data from online sources, features driven by ER such as enriched search verticals, and the uniqueness of noisy and missing data characteristics for each source. We show on real-world and synthetic data that for state-of-the-art techniques, the reality of heterogeneous sources means that the number of labeled training data must scale quadratically in the number of sources, just to maintain constant precision/recall. We address this challenge with a brand new transfer learning algorithm which requires far less training data (or equivalently, achieves superior accuracy with the same data) and is trained using fast convex optimization. The intuition behind our approach is to adaptively share structure learned about one scoring problem with all other scoring problems sharing a data source in common. We demonstrate that our theoretically motivated approach incurs no runtime cost while it can maintain constant precision/recall with the cost of labeling increasing only linearly with the number of sources.

Connected through hyperedge HE-1 (induced by source paper "blocking and filtering techniques for entity resolution a survey"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "pigeonring a principle for faster thresholded similarity search"
    - "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"

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"
    - "reuse and adaptation for entity resolution through transfer learning"
    - "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"

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"
    - "reuse and adaptation for entity resolution through transfer learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "reuse and adaptation for entity resolution through transfer learning"):
  with members:
    - "deep contextualized word representations"
    - "enriching word vectors with subword information"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "crowder crowdsourcing entity resolution"
    - "frustratingly easy domain adaptation"
    - "domain adaptation for statistical classifiers"

Connected through hyperedge HE-5 (induced by source paper "in search of an entity resolution oasis optimal asymptotic sequential importance sampling"):
  with members:
    - "a practioner s guide to evaluating entity resolution results"

Connected through hyperedge HE-6 (induced by source paper "principled graph matching algorithms for integrating multiple data sources"):
  with members:
    - "a hierarchical graphical model for record linkage"
    - "improving entity resolution with global constraints"
    - "human powered sorts and joins"
    - "max product for maximum weight matching convergence correctness and lp duality"

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)