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: "feature engineering for knowledge base construction"
  Abstract: Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student. #R##N#Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user "think about features---not algorithms." We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality.

Connected through hyperedge HE-1 (induced by source paper "fonduer knowledge base construction from richly formatted data"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "multilingual relation extraction using compositional universal schema"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "incremental knowledge base construction using deepdive"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "incremental knowledge base construction using deepdive"):
  with members:
    - "from data fusion to knowledge fusion"
    - "logarithmic time updates and queries in probabilistic networks"
    - "errata distant supervision for relation extraction with matrix completion"
    - "linview incremental view maintenance for complex analytical queries"
    - "dimmwitted a study of main memory statistical analytics"
    - "a convolutional neural network for modelling sentences"
    - "adaptive inference on general graphical models"
    - "incrementally maintaining classification using an rdbms"

Connected through hyperedge HE-3 (induced by source paper "network lasso clustering and optimization in large graphs"):
  with members:
    - "hinge loss markov random fields convex inference for structured prediction"

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)