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 "reverse engineering of communications networks evolution and challenges"):
  Abstract: Reverse engineering of a communications network is the process of identifying the communications protocol used in the network. This problem arises in various situations such as eavesdropping, intelligent jamming, cognitive radio, and adaptive coding and modulation (ACM). According to the Open Systems Interconnection (OSI) reference model, the first step in reverse engineering of communications networks is recognition of physical layer which consists of recognition of digital modulations and identification of physical layer transmission techniques. The next step is recognition of data link layer (consisting of frame synchronization, recognition of channel codes, reconstruction of interleavers, reconstruction of scramblers, etc.) and also recognition of network and transport layers. The final step in reverse engineering of communications networks is recognition of upper layers which essentially can be seen as identification of source encoders. The objective of this paper is to provide a comprehensive overview on the current methods for reverse engineering of communications networks. Furthermore, challenges and open research issues in this field are introduced.

Includes members:
  Member "blind identification of sm and alamouti stbc ofdm signals":
    also connected through other hyperedges:
      (none)
  Member "asynchronous linear modulation classification with multiple sensors via generalized em algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "synthesis by analysis of bch codes":
    also connected through other hyperedges:
      (none)
  Member "computationally efficient modulation level classification based on probability distribution distance functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blind channel estimation and data detection with unknown modulation and coding scheme")

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.IT(Information Theory)