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: "detectability of symbol manipulation by an amplify and forward relay"
  Abstract: This paper studies the problem of detecting a potential malicious relay node by a source node that relies on the relay to forward information to other nodes. The channel model of two source nodes simultaneously sending symbols to a relay is considered. The relay is contracted to forward the symbols that it receives back to the sources in the amplify-and-forward manner. However there is a chance that the relay may send altered symbols back to the sources. Each source attempts to individually detect such malicious acts of the relay by comparing the empirical distribution of the symbols that it receives from the relay conditioned on its own transmitted symbols with known stochastic characteristics of the channel. It is shown that maliciousness of the relay can be asymptotically detected with sufficient channel observations if and only if the channel satisfies a non-manipulable condition, which can be easily checked. As a result, the non-manipulable condition provides us a clear-cut criterion to determine the detectability of the aforementioned class of symbol manipulation attacks potentially conducted by the relay.

Connected through hyperedge HE-1 (induced by source paper "detecting arbitrary attacks using continuous secured side information in wireless networks"):
  with members:
    - "detecting byzantine attacks without clean reference"
    - "a coding approach to guarantee information integrity against a byzantine relay"
    - "strong secrecy and reliable byzantine detection in the presence of an untrusted relay"

Connected through hyperedge HE-2 (induced by source paper "detecting byzantine attacks for gaussian two way relay system"):
  with members:
    - "detecting byzantine attacks without clean reference"
    - "a coding approach to guarantee information integrity against a byzantine relay"
    - "strong secrecy and reliable byzantine detection in the presence of an untrusted relay"

Connected through hyperedge HE-3 (induced by source paper "detecting byzantine attacks without clean reference"):
  with members:
    - "a coding approach to guarantee information integrity against a byzantine relay"
    - "strong secrecy and reliable byzantine detection in the presence of an untrusted relay"

Connected through hyperedge HE-4 (induced by source paper "a coding approach to guarantee information integrity against a byzantine relay"):
  with members:
    - "robust network coding in the presence of untrusted nodes"

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)