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: "a deep relevance matching model for ad hoc retrieval"
  Abstract: In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.

Connected through hyperedge HE-1 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems"):
  with members:
    - "a hybrid retrieval generation neural conversation model"
    - "attentive history selection for conversational question answering"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "learning contextualized document representations for healthcare answer retrieval"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning dense representations for entity retrieval"
    - "a question entailment approach to question answering"
    - "pubmedqa a dataset for biomedical research question answering"
    - "zero shot entity linking by reading entity descriptions"
    - "sector a neural model for coherent topic segmentation and classification"
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "anmm ranking short answer texts with attention based neural matching model"

Connected through hyperedge HE-3 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "bertsel answer selection with pre trained models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "sanity check a strong alignment and information retrieval baseline for question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "neural relation prediction for simple question answering over knowledge graph"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "improved neural relation detection for knowledge base question answering"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "bilateral multi perspective matching for natural language sentences"
    - "attentive pooling networks"
    - "learning to match using local and distributed representations of text for web search"
    - "cfo conditional focused neural question answering with large scale knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "recommending themes for ad creative design via visual linguistic representations"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "advise symbolism and external knowledge for decoding advertisements"
    - "automatic understanding of image and video advertisements"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-8 (induced by source paper "generating scientific question answering corpora from q a forums"):
  with members:
    - "a question entailment approach to question answering"
    - "emrqa a large corpus for question answering on electronic medical records"
    - "biomedical question answering via weighted neural network passage retrieval"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "pacrr a position aware neural ir model for relevance matching"

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)