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: "neural ranking models with weak supervision"
  Abstract: Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection(Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models.

Connected through hyperedge HE-1 (induced by source paper "selective weak supervision for neural information retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "understanding the behaviors of bert in ranking"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-2 (induced by source paper "separate and attend in personal email search"):
  with members:
    - "spherical text embedding"
    - "tf ranking scalable tensorflow library for learning to rank"
    - "area attention"
    - "domain adaptation for enterprise email search"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "overview of the trec 2019 deep learning track"):
  with members:
    - "document expansion by query prediction"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "an updated duet model for passage re ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "learning to match using local and distributed representations of text for web search"
    - "ms marco a human generated machine reading comprehension dataset"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-4 (induced by source paper "ranking significant discrepancies in clinical reports"):
  with members:
    - "cedr contextualized embeddings for document ranking"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "pacrr a position aware neural ir model for relevance matching"
    - "learning generic sentence representations using convolutional neural networks"

Connected through hyperedge HE-5 (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"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-6 (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"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "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-7 (induced by source paper "on sharing models instead of data using mimic learning for smart health applications"):
  with members:
    - "detecting sybil attacks using proofs of work and location in vanets"
    - "privacy preserving smart parking system using blockchain and private information retrieval"
    - "b ride ride sharing with privacy preservation trust and fair payment atop public blockchain"
    - "on the reliability of wireless virtual reality at terahertz thz frequencies"
    - "privacy preserving and collusion resistant charging coordination schemes for smart grid"
    - "mimic learning to generate a shareable network intrusion detection model"
    - "expansion of cyber attack data from unbalanced datasets using generative techniques"
    - "mobility in the sky performance and mobility analysis for cellular connected uavs"

Connected through hyperedge HE-8 (induced by source paper "revealing the importance of semantic retrieval for machine reading at scale"):
  with members:
    - "cognitive graph for multi hop reading comprehension at scale"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "automated fact checking task formulations methods and future directions"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"

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)