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: "modeling diverse relevance patterns in ad hoc retrieval"
  Abstract: Assessing relevance between a query and a document is challenging in ad-hoc retrieval due to its diverse patterns, i.e., a document could be relevant to a query as a whole or partially as long as it provides sufficient information for users' need. Such diverse relevance patterns require an ideal retrieval model to be able to assess relevance in the right granularity adaptively. Unfortunately, most existing retrieval models compute relevance at a single granularity, either document-wide or passage-level, or use fixed combination strategy, restricting their ability in capturing diverse relevance patterns. In this work, we propose a data-driven method to allow relevance signals at different granularities to compete with each other for final relevance assessment. Specifically, we propose a HIerarchical Neural maTching model (HiNT) which consists of two stacked components, namely local matching layer and global decision layer. The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document. The global decision layer accumulates local signals into different granularities and allows them to compete with each other to decide the final relevance score.Experimental results demonstrate that our HiNT model outperforms existing state-of-the-art retrieval models significantly on benchmark ad-hoc retrieval datasets.

Connected through hyperedge HE-1 (induced by source paper "interpretable time budget constrained contextualization for re ranking"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "passage re ranking with bert"
    - "a study on the interpretability of neural retrieval models using deepshap"
    - "document expansion by query prediction"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "an updated duet model for passage re ranking"
    - "let s measure run time extending the ir replicability infrastructure to include performance aspects"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-2 (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-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"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "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 "wikir a python toolkit for building a large scale wikipedia based english information retrieval dataset"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "a deep look into neural ranking models for information retrieval"
    - "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"
    - "neural ranking models with weak supervision"

Connected through hyperedge HE-5 (induced by source paper "corpus level end to end exploration for interactive systems"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "deeptilebars visualizing term distribution for neural information retrieval"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "a reinforcement learning driven translation model for search oriented conversational systems"
    - "coqa a conversational question answering challenge"
    - "proximal policy optimization algorithms"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "visualizing and understanding neural models in nlp"

Connected through hyperedge HE-6 (induced by source paper "a passage based approach to learning to rank documents"):
  with members:
    - "neural ranking models with weak supervision"
    - "fast and accurate annotation of short texts with wikipedia pages"
    - "efficient and effective spam filtering and re ranking for large web datasets"

Connected through hyperedge HE-7 (induced by source paper "deeptilebars visualizing term distribution for neural information retrieval"):
  with members:
    - "attention based neural text segmentation"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "learning visual features from snapshots for web search"
    - "pacrr a position aware neural ir model for relevance matching"
    - "dsrim a deep neural information retrieval model enhanced by a knowledge resource driven representation of documents"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"

Connected through hyperedge HE-8 (induced by source paper "nprf a neural pseudo relevance feedback framework for ad hoc information retrieval"):
  with members:
    - "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"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "learning to match using local and distributed representations of text for web search"
    - "adam a method for stochastic optimization"

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)