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: "query expansion based on crowd knowledge for code search"
  Abstract: As code search is a frequent developer activity in software development practices, improving the performance of code search is a critical task. In the text retrieval based search techniques employed in the code search, the term mismatch problem is a critical language issue for retrieval effectiveness. By reformulating the queries, query expansion provides effective ways to solve the term mismatch problem. In this paper, we propose Query Expansion based on Crowd Knowledge (QECK), a novel technique to improve the performance of code search algorithms. QECK identifies software-specific expansion words from the high quality pseudo relevance feedback question and answer pairs on Stack Overflow to automatically generate the expansion queries. Furthermore, we incorporate QECK in the classic Rocchio's model, and propose QECK based code search method  QECKRocchio . We conduct three experiments to evaluate our QECK technique and investigate  QECKRocchio  in a large-scale corpus containing real-world code snippets and a question and answer pair collection. The results show that QECK improves the performance of three code search algorithms by up to 64 percent in Precision, and 35 percent in NDCG. Meanwhile, compared with the state-of-the-art query expansion method, the improvement of  QECK Rocchio  is 22 percent in Precision, and 16 percent in NDCG.

Connected through hyperedge HE-1 (induced by source paper "boosting api recommendation with implicit feedback"):
  with members:
    - "recommending comprehensive solutions for programming tasks by mining crowd knowledge"
    - "aroma code recommendation via structural code search"
    - "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"
    - "bridging semantic gaps between natural languages and apis with word embedding"
    - "webapirec recommending web apis to software projects via personalized ranking"
    - "xgboost a scalable tree boosting system"
    - "parameter free probabilistic api mining across github"
    - "rack automatic api recommendation using crowdsourced knowledge"

Connected through hyperedge HE-2 (induced by source paper "query expansion techniques for information retrieval a survey"):
  with members:
    - "just an update on pming distance for web based semantic similarity in artificial intelligence and data mining"
    - "using word embeddings for automatic query expansion"
    - "query expansion with locally trained word embeddings"
    - "query expansion via structural motifs in wikipedia graph"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "exploring query categorisation for query expansion a study"
    - "query expansion using term distribution and term association"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "rosf leveraging information retrieval and supervised learning for recommending code snippets"):
  with members:
    - "a more accurate model for finding tutorial segments explaining apis"
    - "link prediction in social networks the state of the art"

Connected through hyperedge HE-4 (induced by source paper "improving automatic source code summarization via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "sentence simplification with deep reinforcement learning"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "tree to sequence attentional neural machine translation"
    - "deep reinforcement learning for dialogue generation"
    - "from query to usable code an analysis of stack overflow code snippets"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"):
  with members:
    - "strict information retrieval based search term identification for concept location"
    - "improved query reformulation for concept location using coderank and document structures"
    - "nlp2code code snippet content assist via natural language tasks"
    - "enriching word vectors with subword information"
    - "rack automatic api recommendation using crowdsourced knowledge"
    - "quickar automatic query reformulation for concept location using crowdsourced knowledge"
    - "towards a context aware ide based meta search engine for recommendation about programming errors and exceptions"
    - "on the use of context in recommending exception handling code examples"

Connected through hyperedge HE-6 (induced by source paper "bridging semantic gaps between natural languages and apis with word embedding"):
  with members:
    - "nlp2code code snippet content assist via natural language tasks"
    - "swim synthesizing what i mean code search and idiomatic snippet synthesis"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "the google similarity distance"

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.SE(Software Engineering)