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 multiple document fields"
  Abstract: Deep neural networks have recently shown promise in the ad-hoc retrieval task. However, such models have often been based on one field of the document, for example considering document title only or document body only. Since in practice documents typically have multiple fields, and given that non-neural ranking models such as BM25F have been developed to take advantage of document structure, this paper investigates how neural models can deal with multiple document fields. We introduce a model that can consume short text fields such as document title and long text fields such as document body. It can also handle multi-instance fields with variable number of instances, for example where each document has zero or more instances of incoming anchor text. Since fields vary in coverage and quality, we introduce a masking method to handle missing field instances, as well as a field-level dropout method to avoid relying too much on any one field. As in the studies of non-neural field weighting, we find it is better for the ranker to score the whole document jointly, rather than generate a per-field score and aggregate. We find that different document fields may match different aspects of the query and therefore benefit from comparing with separate representations of the query text. The combination of techniques introduced here leads to a neural ranker that can take advantage of full document structure, including multiple instance and missing instance data, of variable length. The techniques significantly enhance the performance of the ranker, and also outperform a learning to rank baseline with hand-crafted features.

Connected through hyperedge HE-1 (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"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "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-2 (induced by source paper "generic intent representation in web search"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "relevance based word embedding"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-3 (induced by source paper "overcoming low utility facets for complex answer retrieval"):
  with members:
    - "characterizing question facets for complex answer retrieval"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "word entity duet representations for document ranking"
    - "benchmark for complex answer retrieval"
    - "pacrr a position aware neural ir model for relevance matching"
    - "task oriented query reformulation with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"):
  with members:
    - "passage re ranking with bert"
    - "an updated duet model for passage re ranking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "benchmark for complex answer retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-5 (induced by source paper "an updated duet model for passage re ranking"):
  with members:
    - "passage re ranking with bert"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "benchmark for complex answer retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "ms marco a human generated machine reading comprehension dataset"
    - "exploring the limits of language modeling"
    - "character aware neural language models"

Connected through hyperedge HE-6 (induced by source paper "an axiomatic approach to regularizing neural ranking models"):
  with members:
    - "passage re ranking with bert"
    - "physics informed regularization of deep neural networks"
    - "data augmentation for low resource neural machine translation"
    - "neural ranking models with weak supervision"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "learning to match using local and distributed representations of text for web search"
    - "ms marco a human generated machine reading comprehension dataset"
    - "label free supervision of neural networks with physics and domain knowledge"

Connected through hyperedge HE-7 (induced by source paper "neural document expansion with user feedback"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "attention is all you need"
    - "end to end learning for short text expansion"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-8 (induced by source paper "cross domain regularization for neural ranking models using adversarial learning"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "benchmark for complex answer retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "lstm based deep learning models for non factoid answer selection"
    - "deep domain confusion maximizing for domain invariance"

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)