human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"):
  Abstract: Given a closed-source program, such as most of proprietary software and viruses, binary code analysis is indispensable for many tasks, such as code plagiarism detection and malware analysis. Today, source code is very often compiled for various architectures, making cross-architecture binary code analysis increasingly important. A binary, after being disassembled, is expressed in an assembly languages. Thus, recent work starts exploring Natural Language Processing (NLP) inspired binary code analysis. In NLP, words are usually represented in high-dimensional vectors (i.e., embeddings) to facilitate further processing, which is one of the most common and critical steps in many NLP tasks. We regard instructions as words in NLP-inspired binary code analysis, and aim to represent instructions as embeddings as well. #R##N#To facilitate cross-architecture binary code analysis, our goal is that similar instructions, regardless of their architectures, have embeddings close to each other. To this end, we propose a joint learning approach to generating instruction embeddings that capture not only the semantics of instructions within an architecture, but also their semantic relationships across architectures. To the best of our knowledge, this is the first work on building cross-architecture instruction embedding model. As a showcase, we apply the model to resolving one of the most fundamental problems for binary code similarity comparison---semantics-based basic block comparison, and the solution outperforms the code statistics based approach. It demonstrates that it is promising to apply the model to other cross-architecture binary code analysis tasks.

Includes members:
  Member "sorting and transforming program repair ingredients via deep learning code similarities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "checking smart contracts with structural code embedding")
  Member "a stronger baseline for multilingual word embeddings":
    also connected through other hyperedges:
      (none)
  Member "neural machine translation inspired binary code similarity comparison beyond function pairs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards learning representations of binary executable files for security tasks")
  Member "neural network based graph embedding for cross platform binary code similarity detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")
  Member "deep learning for answer sentence selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection")
  Member "learning bilingual word representations by marginalizing alignments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised cross lingual transfer of word embedding spaces")
  Member "convolutional neural networks over tree structures for programming language processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial robustness for code")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")

Question: Please tell me which class the source paper 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.CR(Cryptography and Security)