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: "off the beaten path let s replace term based retrieval with k nn search"
  Abstract: Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online.

Connected through hyperedge HE-1 (induced by source paper "overview of the trec 2019 deep learning track"):
  with members:
    - "document expansion by query prediction"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "an updated duet model for passage re ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "neural ranking models with weak supervision"
    - "learning to match using local and distributed representations of text for web search"
    - "ms marco a human generated machine reading comprehension dataset"

Connected through hyperedge HE-2 (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-3 (induced by source paper "context aware sentence passage term importance estimation for first stage retrieval"):
  with members:
    - "passage re ranking with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "document expansion by query prediction"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "an updated duet model for passage re ranking"
    - "on the effect of low frequency terms on neural ir models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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"
    - "benchmark for complex answer retrieval"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-5 (induced by source paper "the performance envelope of inverted indexing on modern hardware"):
  with members:
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "dynamic memory allocation policies for postings in real time twitter search"

Connected through hyperedge HE-6 (induced by source paper "neural vector spaces for unsupervised information retrieval"):
  with members:
    - "neural networks for information retrieval"
    - "semantic entity retrieval toolkit"
    - "efficient vector representation for documents through corruption"
    - "pyndri a python interface to the indri search engine"
    - "reply with proactive recommendation of email attachments"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "noisy activation functions"

Connected through hyperedge HE-7 (induced by source paper "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"):
  with members:
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "billion scale similarity search with gpus"
    - "graph based time space trade offs for approximate near neighbors"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "hybrid lsh faster near neighbors reporting in high dimensional space"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-8 (induced by source paper "semantic entity retrieval toolkit"):
  with members:
    - "pyndri a python interface to the indri search engine"
    - "unsupervised efficient and semantic expertise retrieval"
    - "learning latent vector spaces for product search"

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)