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: "cross domain regularization for neural ranking models using adversarial learning"
  Abstract: Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly from the data, however, may come at a price. Without any special supervision, these models learn relationships that may hold only in the domain from which the training data is sampled, and generalize poorly to domains not observed during training. We study the effectiveness of adversarial learning as a cross domain regularizer in the context of the ranking task. We use an adversarial discriminator and train our neural ranking model on a small set of domains. The discriminator provides a negative feedback signal to discourage the model from learning domain specific representations. Our experiments show consistently better performance on held out domains in the presence of the adversarial discriminator---sometimes up to 30% on precision@1.

Connected through hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning a deep listwise context model for ranking refinement"

Connected through hyperedge HE-2 (induced by source paper "domain adaptation for enterprise email search"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "neural ranking models with weak supervision"
    - "adversarial discriminative domain adaptation"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"
    - "neural models for information retrieval"
    - "deep reconstruction classification networks for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "a domain generalization perspective on listwise context modeling"):
  with members:
    - "globally optimized mutual influence aware ranking in e commerce search"
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "generalizing across domains via cross gradient training"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "learning to generalize meta learning for domain generalization"
    - "unified deep supervised domain adaptation and generalization"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-4 (induced by source paper "duet at trec 2019 deep learning track"):
  with members:
    - "overview of the trec 2019 deep learning track"
    - "passage re ranking with bert"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "an updated duet model for passage re ranking"
    - "neural ranking models with multiple document fields"
    - "benchmark for complex answer retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "a dual embedding space model for document ranking"

Connected through hyperedge HE-5 (induced by source paper "zero shot entity linking by reading entity descriptions"):
  with members:
    - "multi source domain adaptation with mixture of experts"
    - "improving entity linking by modeling latent relations between mentions"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge based word sense disambiguation using topic models"
    - "universal language model fine tuning for text classification"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

Connected through hyperedge HE-6 (induced by source paper "to reverse the gradient or not an empirical comparison of adversarial and multi task learning in speech recognition"):
  with members:
    - "domain adversarial training for accented speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "letter based speech recognition with gated convnets"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an overview of multi task learning in deep neural networks"
    - "fader networks manipulating images by sliding attributes"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"

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.IR(Information Retrieval)