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: "path based function embedding and its application to specification mining"
  Abstract: Identifying the relationships among program elements is useful for program understanding, debugging, and analysis. One such relationship is synonymy. Function synonyms are functions that play a similar role in code, e.g. functions that perform initialization for different device drivers, or functions that implement different symmetric-key encryption schemes. Function synonyms are not necessarily semantically equivalent and can be syntactically dissimilar; consequently, approaches for identifying code clones or functional equivalence cannot be used to identify them. This paper presents func2vec, an algorithm that maps each function to a vector in a vector space such that function synonyms are grouped together. We compute the function embedding by training a neural network on sentences generated from random walks over an encoding of the program as a labeled pushdown system (l-PDS). We demonstrate that func2vec is effective at identifying function synonyms in the Linux kernel. Furthermore, we show how function synonyms enable mining error-handling specifications with high support in Linux file systems and drivers.

Connected through hyperedge HE-1 (induced by source paper "learning execution through neural code fusion"):
  with members:
    - "a neural network prefetcher for arbitrary memory access patterns"
    - "neural code comprehension a learnable representation of code semantics"
    - "learning memory access patterns"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "ithemal accurate portable and fast basic block throughput estimation using deep neural networks"
    - "neural arithmetic logic units"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-2 (induced by source paper "a literature study of embeddings on source code"):
  with members:
    - "import2vec learning embeddings for software libraries"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "semantic embeddings for program behavior patterns"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "learning to represent edits"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "automated software vulnerability detection with machine learning"

Connected through hyperedge HE-3 (induced by source paper "neural bug finding a study of opportunities and challenges"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "neural program repair by jointly learning to localize and repair"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "learning to represent edits"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "generative code modeling with graphs"
    - "automated software vulnerability detection with machine learning"

Connected through hyperedge HE-4 (induced by source paper "evaluating semantic representations of source code"):
  with members:
    - "coset a benchmark for evaluating neural program embeddings"
    - "a literature study of embeddings on source code"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "automated software vulnerability detection with machine learning"
    - "code2seq generating sequences from structured representations of code"
    - "semantic code repair using neuro symbolic transformation networks"

Connected through hyperedge HE-5 (induced by source paper "learning the relation between code features and code transforms with structured prediction"):
  with members:
    - "sequencer sequence to sequence learning for end to end program repair"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "towards an automated approach for bug fix pattern detection"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "automatic software repair a bibliography"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to generate corrective patches using neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "arducode predictive framework for automation engineering"):
  with members:
    - "a literature study of embeddings on source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "automated software vulnerability detection with machine learning"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "code vectors understanding programs through embedded abstracted symbolic traces"):
  with members:
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "learning continuous semantic representations of symbolic expressions"
    - "gated graph sequence neural networks"

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)