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: "collective intelligence for smarter api recommendations in python"
  Abstract: Software developers use Application Programming Interfaces (APIs) of libraries and frameworks extensively while writing programs. In this context, the recommendations provided in code completion pop-ups help developers choose the desired methods. The candidate lists recommended by these tools, however, tend to be large, ordered alphabetically and sometimes even incomplete. A fair amount of work has been done recently to improve the relevance of these code completion results, especially for statically typed languages like Java. However, these proposed techniques rely on the static type of the object and are therefore inapplicable for a dynamically typed language like Python. In this paper, we present PyReco, an intelligent code completion system for Python which uses the mined API usages from open source repositories to order the results based on relevance rather than the conventional alphabetic order. To recommend suggestions that are relevant for a working context, a nearest neighbor classifier is used to identify the best matching usage among all the extracted usage patterns. To evaluate the effectiveness of our system, the code completion queries are automatically extracted from projects and tested quantitatively using a ten-fold cross validation technique. The evaluation shows that our approach outperforms the alphabetically ordered API recommendation systems in recommending APIs for standard, as well as, third-party libraries.

Connected through hyperedge HE-1 (induced by source paper "deep transfer learning for source code modeling"):
  with members:
    - "code2vec learning distributed representations of code"
    - "an exploration of dropout with rnns for natural language inference"
    - "autofolding for source code summarization"
    - "attention is all you need"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep api learning"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "pythia ai assisted code completion system"):
  with members:
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "feed forward networks with attention can solve some long term memory problems"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "using temporal data for making recommendations"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "codegru context aware deep learning with gated recurrent unit for source code modeling"):
  with members:
    - "an exploration of dropout with rnns for natural language inference"
    - "abstract syntax networks for code generation and semantic parsing"
    - "deep api learning"
    - "a convolutional attention network for extreme summarization of source code"
    - "adam a method for stochastic optimization"
    - "word2vec parameter learning explained"
    - "mining idioms from source code"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)