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: "crowder crowdsourcing entity resolution"
  Abstract: Entity resolution is central to data integration and data cleaning. Algorithmic approaches have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a more accurate but expensive (and slow) way to bring human insight into the process. Previous work has proposed batching verification tasks for presentation to human workers but even with batching, a human-only approach is infeasible for data sets of even moderate size, due to the large numbers of matches to be tested. Instead, we propose a hybrid human-machine approach in which machines are used to do an initial, coarse pass over all the data, and people are used to verify only the most likely matching pairs. We show that for such a hybrid system, generating the minimum number of verification tasks of a given size is NP-Hard, but we develop a novel two-tiered heuristic approach for creating batched tasks. We describe this method, and present the results of extensive experiments on real data sets using a popular crowdsourcing platform. The experiments show that our hybrid approach achieves both good efficiency and high accuracy compared to machine-only or human-only alternatives.

Connected through hyperedge HE-1 (induced by source paper "crowdsourced collective entity resolution with relational match propagation"):
  with members:
    - "minoaner schema agnostic non iterative massively parallel resolution of web entities"
    - "principled graph matching algorithms for integrating multiple data sources"
    - "lazier than lazy greedy"
    - "leveraging transitive relations for crowdsourced joins"
    - "sigma simple greedy matching for aligning large knowledge bases"
    - "large scale collective entity matching"
    - "paris probabilistic alignment of relations instances and schema"

Connected through hyperedge HE-2 (induced by source paper "curating social media data"):
  with members:
    - "how to work a crowd developing crowd capital through crowdsourcing"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "on the dynamics of social media popularity a youtube case study"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "temporal provenance model tpm model and query language"
    - "extending sparql to support entity grouping and path queries"

Connected through hyperedge HE-3 (induced by source paper "query efficient correlation clustering"):
  with members:
    - "correlation clustering with adaptive similarity queries"
    - "correlation clustering with same cluster queries bounded by optimal cost"
    - "revisiting semi supervised learning with graph embeddings"
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
    - "local correlation clustering"

Connected through hyperedge HE-4 (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"
    - "reuse and adaptation for entity resolution through transfer learning"
    - "snorkel rapid training data creation with weak supervision"
    - "distributed holistic clustering on linked data"
    - "a dnf blocking scheme learner for heterogeneous datasets"

Connected through hyperedge HE-5 (induced by source paper "towards interpretable and learnable risk analysis for entity resolution"):
  with members:
    - "r humo a risk aware human machine cooperation framework for entity resolution with quality guarantees"
    - "deep anomaly detection with outlier exposure"
    - "discriminative active learning"
    - "on calibration of modern neural networks"
    - "holoclean holistic data repairs with probabilistic inference"
    - "concrete problems in ai safety"
    - "why should i trust you explaining the predictions of any classifier"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards extracting highlights from recorded live videos an implicit crowdsourcing approach"):
  with members:
    - "deeplens towards a visual data management system"
    - "video highlight prediction using audience chat reactions"
    - "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments"
    - "real time video highlights for yahoo esports"
    - "video summarization using deep semantic features"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "combining crowd and machines for multi predicate item screening"):
  with members:
    - "crowd based multi predicate screening of papers in literature reviews"
    - "crowdsourcing paper screening in systematic literature reviews"
    - "optimality of belief propagation for crowdsourced classification"
    - "crowdsourcing information extraction for biomedical systematic reviews"
    - "data fusion resolving conflicts from multiple sources"
    - "crowd access path optimization diversity matters"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"

Connected through hyperedge HE-8 (induced by source paper "autoer automated entity resolution using generative modelling"):
  with members:
    - "reuse and adaptation for entity resolution through transfer learning"
    - "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"
    - "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)