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: "neural machine translation inspired binary code similarity comparison beyond function pairs"
  Abstract: Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.

Connected through hyperedge HE-1 (induced by source paper "towards learning representations of binary executable files for security tasks"):
  with members:
    - "graph matching networks for learning the similarity of graph structured objects"
    - "neural code comprehension a learnable representation of code semantics"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "robust and effective malware detection through quantitative data flow graph metrics"
    - "on the naturalness of buggy code"

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"
    - "path based function embedding and its application to specification mining"

Connected through hyperedge HE-3 (induced by source paper "a case study exploiting neural machine translation to translate cuda to opencl"):
  with members:
    - "tree to tree neural networks for program translation"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "a semantics based hybrid approach on binary code similarity comparison"):
  with members:
    - "binmatch a semantics based hybrid approach on binary code clone analysis"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "a survey of binary code similarity"):
  with members:
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "malware lineage in the wild"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-6 (induced by source paper "software ethology an accurate and resilient semantic binary analysis framework"):
  with members:
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "learning natural coding conventions"

Connected through hyperedge HE-7 (induced by source paper "exploiting the inherent limitation of l0 adversarial examples"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "protecting jpeg images against adversarial attacks"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "adversarial robustness toolbox v0 3 0"
    - "generalizable adversarial examples detection based on bi model decision mismatch"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-8 (induced by source paper "a multiversion programming inspired approach to detecting audio adversarial examples"):
  with members:
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "generalizable adversarial examples detection based on bi model decision mismatch"
    - "exploiting the inherent limitation of l0 adversarial examples"

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)