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: "nprf a neural pseudo relevance feedback framework for ad hoc information retrieval"
  Abstract: Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.

Connected through hyperedge HE-1 (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-2 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "generating a common question from multiple documents using multi source encoder decoder models"):
  with members:
    - "passage re ranking with bert"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "deep communicating agents for abstractive summarization"
    - "input combination strategies for multi source transformer decoder"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-4 (induced by source paper "the simplest thing that can possibly work pseudo relevance feedback using text classification"):
  with members:
    - "efficient and effective spam filtering and re ranking for large web datasets"

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)