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: "co pacrr a context aware neural ir model for ad hoc retrieval"
  Abstract: Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signals is also important. Intuitively, when extracting, modeling, and combining matching signals, one would like to consider the surrounding text (local context) as well as other signals from the same document that can contribute to the overall relevance score. In this work, we highlight three potential shortcomings caused by not considering context information and propose three neural ingredients to address them: a disambiguation component, cascade k-max pooling, and a shuffling combination layer. Incorporating these components into the PACRR model yields Co-PACRR, a novel context-aware neural IR model. Extensive comparisons with established models on Trec Web Track data confirm that the proposed model can achieve superior search results. In addition, an ablation analysis is conducted to gain insights into the impact of and interactions between different components. We release our code to enable future comparisons.

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 "multi stage document ranking with bert"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "passage re ranking with bert"
    - "simple applications of bert for ad hoc document retrieval"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "cedr contextualized embeddings for document ranking"
    - "end to end open domain question answering with"

Connected through hyperedge HE-4 (induced by source paper "passage re ranking with bert"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "quasar datasets for question answering by search and reading"
    - "semi supervised sequence tagging with bidirectional language models"
    - "reading wikipedia to answer open domain questions"
    - "simple and effective multi paragraph reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "passage ranking with weak supervision"):
  with members:
    - "passage re ranking with bert"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "measuring the effects of data parallelism on neural network training"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with weak supervision"

Connected through hyperedge HE-6 (induced by source paper "semantic product search"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "pacrr a position aware neural ir model for relevance matching"
    - "a deep relevance matching model for ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "learning to match using local and distributed representations of text for web search"
    - "a dual embedding space model for document ranking"
    - "bag of tricks for efficient text classification"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-7 (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"
    - "neural ranking models with multiple document fields"
    - "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-8 (induced by source paper "improving low resource cross lingual document retrieval by reranking with deep bilingual representations"):
  with members:
    - "deep relevance ranking using enhanced document query interactions"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "pacrr a position aware neural ir model for relevance matching"
    - "deep architectures for neural machine translation"
    - "word translation without parallel data"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

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)