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: "learning joint query interpretation and response ranking"
  Abstract: Thanks to information extraction and semantic Web efforts, search on unstructured text is increasingly refined using semantic annotations and structured knowledge bases. However, most users cannot become familiar with the schema of knowledge bases and ask structured queries. Interpreting free-format queries into a more structured representation is of much current interest. The dominant paradigm is to segment or partition query tokens by purpose (references to types, entities, attribute names, attribute values, relations) and then launch the interpreted query on structured knowledge bases. Given that structured knowledge extraction is never complete, here we use a data representation that retains the unstructured text corpus, along with structured annotations (mentions of entities and relationships) on it. We propose two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus.One, inspired by probabilistic language models, computes expected response scores over the uncertainties of query interpretation. The other is based on max-margin discriminative learning, with latent variables representing those uncertainties. In the context of typed entity search, both formulations bridge a considerable part of the accuracy gap between a generic query that does not constrain the type at all, and the upper bound where the "perfect" target entity type of each query is provided by humans. Our formulations are also superior to a two-stage approach of first choosing a target type using recent query type prediction techniques, and then launching a type-restricted entity search query.

Connected through hyperedge HE-1 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "answering complex questions by joining multi document evidence with quasi knowledge graphs"):
  with members:
    - "the web as a knowledge base for answering complex questions"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "reading wikipedia to answer open domain questions"
    - "answering complex questions using open information extraction"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-3 (induced by source paper "neural architecture for question answering using a knowledge graph and web corpus"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deep joint entity disambiguation with local neural attention"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "word entity duet representations for document ranking"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "on the sample complexity of end to end training vs semantic abstraction training"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-4 (induced by source paper "target type identification for entity bearing queries"):
  with members:
    - "design patterns for fusion based object retrieval"

Connected through hyperedge HE-5 (induced by source paper "on type aware entity retrieval"):
  with members:
    - "target type identification for entity bearing queries"

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)