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: "benchmark for complex answer retrieval"
  Abstract: Retrieving paragraphs to populate a Wikipedia article is a challenging task. The new TREC Complex Answer Retrieval (TREC CAR) track introduces a comprehensive dataset that targets this retrieval scenario. We present early results from a variety of approaches -- from standard information retrieval methods (e.g., tf-idf) to complex systems that using query expansion using knowledge bases and deep neural networks. The goal is to offer future participants of this track an overview of some promising approaches to tackle this problem.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sector a neural model for coherent topic segmentation and classification"):
  with members:
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "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"
    - "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 "duet at trec 2019 deep learning track"):
  with members:
    - "overview of the trec 2019 deep learning track"
    - "passage re ranking with bert"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "an updated duet model for passage re ranking"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "neural ranking models with multiple document fields"
    - "learning to match using local and distributed representations of text for web search"
    - "a dual embedding space model for document ranking"

Connected through hyperedge HE-4 (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"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "word entity duet representations for document ranking"
    - "pacrr a position aware neural ir model for relevance matching"
    - "task oriented query reformulation with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"):
  with members:
    - "passage re ranking with bert"
    - "an updated duet model for passage re ranking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with multiple document fields"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-6 (induced by source paper "an updated duet model for passage re ranking"):
  with members:
    - "passage re ranking with bert"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with multiple document fields"
    - "learning to match using local and distributed representations of text for web search"
    - "ms marco a human generated machine reading comprehension dataset"
    - "exploring the limits of language modeling"
    - "character aware neural language models"

Connected through hyperedge HE-7 (induced by source paper "content based weak supervision for ad hoc re ranking"):
  with members:
    - "overcoming low utility facets for complex answer retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with weak supervision"
    - "pacrr a position aware neural ir model for relevance matching"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-8 (induced by source paper "semantic driven fielded entity retrieval"):
  with members:
    - "word entity duet representations for document ranking"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "distributed representations of words and phrases and their compositionality"

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)