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 "deep rnn oriented paradigm shift through bocanet broken obfuscated circuit attack"):
  Abstract: This is the first work augmenting hardware attacks mounted on obfuscated circuits by incorporating deep recurrent neural network (D-RNN). Logic encryption obfuscation has been used for thwarting counterfeiting, overproduction, and reverse engineering but vulnerable to attacks. There have been efficient schemes, e.g., satisfiability-checking (SAT) based attack, which can potentially compromise hardware obfuscation circuits. Nevertheless, not only there exist countermeasures against such attacks in the state-of-the-art (including the recent delay+logic locking (DLL) scheme in DAC'17), but the sheer amount of time/resources to mount the attack could hinder its efficacy. In this paper, we propose a deep RNN-oriented approach, called BOCANet, to (i) compromise the obfuscated hardware at least an order-of magnitude more efficiently (>20X faster with relatively high success rate) compared to existing attacks; (ii) attack such locked hardware even when the resources to the attacker are only limited to insignificant number of I/O pairs (< 0.5\%) to reconstruct the secret key; and (iii) break a number of experimented benchmarks (ISCAS-85 c423, c1355, c1908, and c7552) successfully.

Includes members:
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")

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)