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: "an approach for weakly supervised deep information retrieval"
  Abstract: Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection.

Connected through hyperedge HE-1 (induced by source paper "passage ranking with weak supervision"):
  with members:
    - "passage re ranking with bert"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "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"

Connected through hyperedge HE-2 (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"
    - "benchmark for complex answer 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)