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: "crowdsourcing ground truth for medical relation extraction"
  Abstract: Cognitive computing systems require human-labeled data for evaluation and often for training. The standard practice used in gathering this data minimizes disagreement between annotators, and we have found this results in data that fails to account for the ambiguity inherent in language. We have proposed the CrowdTruth method for collecting ground truth through crowdsourcing, which reconsiders the role of people in machine learning based on the observation that disagreement between annotators provides a useful signal for phenomena such as ambiguity in the text. We report on using this method to build an annotated data set for medical relation extraction for the cause and treat relations and how this data performed in a supervised training experiment. We demonstrate that by modeling ambiguity, labeled data gathered from crowd workers can (1) reach the level of quality of domain experts for this task, while reducing the cost, and (2) provide better training data at scale than distant supervision. We further propose and validate new weighted measures for precision, recall, and F-measure, which account for ambiguity in both human and machine performance on this task.

Connected through hyperedge HE-1 (induced by source paper "why does a visual question have different answers"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "an analysis of visual question answering algorithms"
    - "learning to disambiguate by asking discriminative questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "crowdsourcing question answer meaning representations"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"

Connected through hyperedge HE-2 (induced by source paper "predicting annotation difficulty to improve task routing and model performance for biomedical information extraction"):
  with members:
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "syntactic patterns improve information extraction for medical search"
    - "attention is all you need"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "metrology for ai from benchmarks to instruments"):
  with members:
    - "discovering blind spots in reinforcement learning"
    - "factors influencing the surprising instability of word embeddings"
    - "enriching word vectors with subword information"
    - "exposing ambiguities in a relation extraction gold standard with crowdsourcing"
    - "comprehensive and reliable crowd assessment algorithms"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "adversarial learning for chinese ner from crowd annotations"):
  with members:
    - "adversarial multi criteria learning for chinese word segmentation"
    - "aspect augmented adversarial networks for domain adaptation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-5 (induced by source paper "medtruth a semi supervised approach to discovering knowledge condition information from multi source medical data"):
  with members:
    - "does william shakespeare really write hamlet knowledge representation learning with confidence"
    - "truth finding on the deep web is the problem solved"
    - "a survey on truth discovery"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "when truth discovery meets medical knowledge graph estimating trustworthiness degree for medical knowledge condition"):
  with members:
    - "truth finding on the deep web is the problem solved"
    - "a survey on truth discovery"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "empirical methodology for crowdsourcing ground truth"):
  with members:
    - "crowd disagreement of medical images is informative"
    - "on the job learning with bayesian decision theory"

Connected through hyperedge HE-8 (induced by source paper "false positive and cross relation signals in distant supervision data"):
  with members:
    - "classifying relations by ranking with convolutional neural networks"

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.CL(Computation and Language)