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: "fidelity weighted learning"
  Abstract: Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.

Connected through hyperedge HE-1 (induced by source paper "a robust learning approach to domain adaptive object detection"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "meta weight net learning an explicit mapping for sample weighting"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "reptile a scalable metalearning algorithm"
    - "gradient agreement as an optimization objective for meta learning"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-3 (induced by source paper "zero shot knowledge transfer via adversarial belief matching"):
  with members:
    - "dataset distillation"
    - "variational information distillation for knowledge transfer"
    - "zero shot knowledge distillation in deep networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "knowledge distillation from few samples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-4 (induced by source paper "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "on first order meta learning algorithms"
    - "learning to teach with dynamic loss functions"
    - "gradient agreement as an optimization objective for meta learning"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-5 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "co sampling training robust networks for extremely noisy supervision"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a sequential matching framework for multi turn response selection in retrieval based chatbots"
    - "a persona based neural conversation model"

Connected through hyperedge HE-6 (induced by source paper "incidental supervision from question answering signals"):
  with members:
    - "partial or complete that s the question"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"

Connected through hyperedge HE-7 (induced by source paper "named entity recognition with partially annotated training data"):
  with members:
    - "training a neural network in a low resource setting on automatically annotated noisy data"
    - "exploiting partially annotated data for temporal relation extraction"
    - "snorkel rapid training data creation with weak supervision"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised semantic image segmentation with self correcting networks"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "joint optimization framework for learning with noisy labels"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"

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.LG(Machine Learning)