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: "robust rate adaptive wireless communication using ack nak feedback"
  Abstract: To combat the detrimental effects of the variability in wireless channels, we consider cross-layer rate adaptation based on limited feedback. In particular, based on limited feedback in the form of link-layer acknowledgements (ACK) and negative acknowledgements (NAK), we maximize the physical-layer transmission rate subject to an upper bound on the expected packet error rate. \textr{We take a robust approach in that we do not assume} any particular prior distribution on the channel state. We first analyze the fundamental limitations of such systems and derive an upper bound on the achievable rate for signaling schemes based on uncoded QAM and random Gaussian ensembles. We show that, for channel estimation based on binary ACK/NAK feedback, it may be preferable to use a separate training sequence at high error rates, rather than to exploit low-error-rate data packets themselves. We also develop an adaptive recursive estimator, which is provably asymptotically optimal and asymptotically efficient.

Connected through hyperedge HE-1 (induced by source paper "reinforcement based data transmission in temporally correlated fading channels partial csit scenario"):
  with members:
    - "green communication via power optimized harq protocols"
    - "on noisy arq in block fading channels"
    - "on the benefits of partial channel state information for repetition protocols in block fading channels"
    - "coding versus arq in fading channels how reliable should the phy be"
    - "performance of hybrid arq in block fading channels a fixed outage probability analysis"
    - "mimo broadcast channels with finite rate feedback"
    - "limited feedback beamforming over temporally correlated channels"

Connected through hyperedge HE-2 (induced by source paper "control of wireless networks with secrecy"):
  with members:
    - "a secure communication game with a relay helping the eavesdropper"
    - "interference assisted secret communication"
    - "on cooperative relaying schemes for wireless physical layer security"
    - "joint relay and jammer selection for secure two way relay networks"
    - "the secrecy capacity region of the gaussian mimo multi receiver wiretap channel"
    - "optimal scheduling for fair resource allocation in ad hoc networks with elastic and inelastic traffic"
    - "towards the secrecy capacity of the gaussian mimo wire tap channel the 2 2 1 channel"
    - "the wiretap channel with feedback encryption over the channel"

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)