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: "deep relevance ranking using enhanced document query interactions"
  Abstract: We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR's (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.

Connected through hyperedge HE-1 (induced by source paper "mrnn a multi resolution neural network with duplex attention for document retrieval in the context of question answering"):
  with members:
    - "a multi resolution word embedding for document retrieval from large unstructured knowledge bases"
    - "training a ranking function for open domain question answering"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text embeddings for retrieval from a large knowledge base"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "large scale question answering using tourism data"):
  with members:
    - "an updated duet model for passage re ranking"
    - "eli5 long form question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "universal sentence encoder"
    - "quac question answering in context"

Connected through hyperedge HE-3 (induced by source paper "simple attention based representation learning for ranking short social media posts"):
  with members:
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "making neural qa as simple as possible but not simpler"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "embedding biomedical ontologies by jointly encoding network structure and textual node descriptors"):
  with members:
    - "abusive language detection with graph convolutional networks"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "node2vec scalable feature learning for networks"
    - "a general framework for content enhanced network representation learning"
    - "bag of tricks for efficient text classification"
    - "deep residual learning for image recognition"
    - "line large scale information network embedding"

Connected through hyperedge HE-5 (induced by source paper "transfer learning for causal sentence detection"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "extreme multi label legal text classification a case study in eu legislation"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial training for multi context joint entity and relation extraction"

Connected through hyperedge HE-6 (induced by source paper "improving low resource cross lingual document retrieval by reranking with deep bilingual representations"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "cedr contextualized embeddings for document ranking"):
  with members:
    - "passage re ranking with bert"
    - "end to end open domain question answering with"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "optimizing query evaluations using reinforcement learning for web search"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "attention is all you need"
    - "neural ranking models with weak supervision"

Connected through hyperedge HE-8 (induced by source paper "a study on the interpretability of neural retrieval models using deepshap"):
  with members:
    - "exs explainable search using local model agnostic interpretability"
    - "interpreting search result rankings through intent modeling"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "matchzoo a toolkit for deep text matching"
    - "a deep relevance matching model for ad hoc retrieval"
    - "why should i trust you explaining the predictions of any classifier"

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)