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: "characterizing and avoiding negative transfer"
  Abstract: When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferring knowledge from a less related source, it may inversely hurt the target performance, a phenomenon known as negative transfer. Despite its pervasiveness, negative transfer is usually described in an informal manner, lacking rigorous definition, careful analysis, or systematic treatment. This paper proposes a formal definition of negative transfer and analyzes three important aspects thereof. Stemming from this analysis, a novel technique is proposed to circumvent negative transfer by filtering out unrelated source data. Based on adversarial networks, the technique is highly generic and can be applied to a wide range of transfer learning algorithms. The proposed approach is evaluated on six state-of-the-art deep transfer methods via experiments on four benchmark datasets with varying levels of difficulty. Empirically, the proposed method consistently improves the performance of all baseline methods and largely avoids negative transfer, even when the source data is degenerate.

Connected through hyperedge HE-1 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"

Connected through hyperedge HE-2 (induced by source paper "a balanced and uncertainty aware approach for partial domain adaptation"):
  with members:
    - "complement objective training"
    - "tackling partial domain adaptation with self supervision"
    - "learning to transfer examples for partial domain adaptation"
    - "virtual mixup training for unsupervised domain adaptation"
    - "transfer adaptation learning a decade survey"
    - "semi supervised learning by augmented distribution alignment"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "depara deep attribution graph for deep knowledge transferability"):
  with members:
    - "relational knowledge distillation"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "deep model transferability from attribution maps"
    - "similarity preserving knowledge distillation"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-4 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "provable guarantees for gradient based meta learning"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-5 (induced by source paper "customized video qoe estimation with algorithm agnostic transfer learning"):
  with members:
    - "deep leakage from gradients"
    - "federated user representation learning"
    - "privacy preserving qoe modeling using collaborative learning"
    - "a unified approach to interpreting model predictions"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "a review of domain adaptation without target labels"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "theoretical perspective of deep domain adaptation"
    - "deep visual domain adaptation a survey"
    - "detecting and correcting for label shift with black box predictors"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework"):
  with members:
    - "adversarial learning with contextual embeddings for zero resource cross lingual classification and ner"
    - "a survey of cross lingual word embedding models"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "density matching for bilingual word embedding"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"

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)