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: "efficient codes for limited view adversarial channels"
  Abstract: We introduce randomized Limited View (LV) adversary codes that provide protection against an adversary that uses their partial view of the communication to construct an adversarial error vector to be added to the channel. For a codeword of length N, the adversary selects a subset of \rho_rN of the codeword components to "see", and then "adds" an adversarial error vector of weight \rho_wN to the codeword. Performance of the code is measured by the probability of the decoder failure in recovering the sent message. An (N, q^{RN},\delta)-limited view adversary code ensures that the success chance of the adversary in making decoder fail, is bounded by \delta when the information rate of the code is at least R. Our main motivation to study these codes is providing protection for wireless communication at the physical layer of networks. #R##N#We formalize the definition of adversarial error and decoder failure, construct a code with efficient encoding and decoding that allows the adversary to, depending on the code rate, read up to half of the sent codeword and add error on the same coordinates. The code is non-linear, has an efficient decoding algorithm, and is constructed using a message authentication code (MAC) and a Folded Reed-Solomon (FRS) code. The decoding algorithm uses an innovative approach that combines the list decoding algorithm of the FRS codes and the MAC verification algorithm to eliminate the exponential size of the list output from the decoding algorithm. We discuss application of our results to Reliable Message Transmission problem, and open problems for future work.

Connected through hyperedge HE-1 (induced by source paper "sufficiently myopic adversaries are blind"):
  with members:
    - "a channel under simultaneous jamming and eavesdropping attack correlated random coding capacities under strong secrecy criteria"
    - "on the capacity of the binary adversarial wiretap channel"
    - "arbitrarily varying wiretap channels with type constrained states"
    - "the arbitrarily varying wiretap channel secret randomness stability and super activation"
    - "coding against a limited view adversary the effect of causality and feedback"
    - "semantic security capacity for wiretap channels of type ii"
    - "the arbitrarily varying wiretap channel deterministic and correlated random coding capacities under the strong secrecy criterion"
    - "a characterization of the capacity of online causal binary channels"

Connected through hyperedge HE-2 (induced by source paper "adversarial wiretap channel with public discussion"):
  with members:
    - "capacity results for arbitrarily varying wiretap channels"
    - "on optimal secure message transmission by public discussion"
    - "invertible extractors and wiretap protocols"
    - "the wiretap channel with feedback encryption over the channel"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

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.IT(Information Theory)