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: "obfuscation using encryption"
  Abstract: Protecting source code against reverse engineering and theft is an important problem. The goal is to carry out computations using confidential algorithms on an untrusted party while ensuring confidentiality of algorithms. This problem has been addressed for Boolean circuits known as `circuit privacy'. Circuits corresponding to real-world programs are impractical. Well-known obfuscation techniques are highly practicable, but provide only limited security, e.g., no piracy protection. In this work, we modify source code yielding programs with adjustable performance and security guarantees ranging from indistinguishability obfuscators to (non-secure) ordinary obfuscation. The idea is to artificially generate `misleading' statements. Their results are combined with the outcome of a confidential statement using encrypted \emph{selector variables}. Thus, an attacker must `guess' the encrypted selector variables to disguise the confidential source code. We evaluated our method using more than ten programmers as well as pattern mining across open source code repositories to gain insights of (micro-)coding patterns that are relevant for generating misleading statements. The evaluation reveals that our approach is effective in that it successfully preserves source code confidentiality.

Connected through hyperedge HE-1 (induced by source paper "deepobfuscode source code obfuscation through sequence to sequence networks"):
  with members:
    - "abstractive and extractive text summarization using document context vector and recurrent neural networks"
    - "attention based encoder decoder networks for spelling and grammatical error correction"
    - "cryptodl deep neural networks over encrypted data"
    - "assessment of source code obfuscation techniques"
    - "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.CR(Cryptography and Security)