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: "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
  Abstract: With the rise in mobile and voice search, answer passage retrieval acts as a critical component of an effective information retrieval system for open domain question answering. Currently, there are no comparable collections that address non-factoid question answering within larger documents while simultaneously providing enough examples sufficient to train a deep neural network. In this paper, we introduce a new Wikipedia based collection specific for non-factoid answer passage retrieval containing thousands of questions with annotated answers and show benchmark results on a variety of state of the art neural architectures and retrieval models. The experimental results demonstrate the unique challenges presented by answer passage retrieval within topically relevant documents for future research.

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"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "learning a deep listwise context model for ranking refinement"

Connected through hyperedge HE-2 (induced by source paper "sector a neural model for coherent topic segmentation and classification"):
  with members:
    - "characterizing question facets for complex answer retrieval"
    - "structuring wikipedia articles with section recommendations"
    - "text segmentation as a supervised learning task"
    - "do convolutional networks need to be deep for text classification"
    - "benchmark for complex answer retrieval"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "joint learning of answer selection and answer summary generation in community question answering"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "neural document summarization by jointly learning to score and select sentences"
    - "wikihow a large scale text summarization dataset"
    - "joint multitask learning for community question answering using task specific embeddings"
    - "multi task learning with multi view attention for answer selection and knowledge base question answering"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"

Connected through hyperedge HE-4 (induced by source paper "universal text representation from bert an empirical study"):
  with members:
    - "assessing bert s syntactic abilities"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "searchqa a new q a dataset augmented with context from a search engine"

Connected through hyperedge HE-5 (induced by source paper "passage ranking with weak supervision"):
  with members:
    - "passage re ranking with bert"
    - "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"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"

Connected through hyperedge HE-6 (induced by source paper "antique a non factoid question answering benchmark"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2017 task 3 community question answering"
    - "investigating the successes and failures of bert for passage re ranking"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a deep relevance matching model for ad hoc retrieval"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"

Connected through hyperedge HE-7 (induced by source paper "a study of bert for non factoid question answering under passage length constraints"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "an updated duet model for passage re ranking"
    - "understanding the behaviors of bert in ranking"
    - "investigating the successes and failures of bert for passage re ranking"
    - "end to end open domain question answering with"
    - "attention is all you need"
    - "lstm based deep learning models for non factoid answer selection"

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)