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: "perturbation based distributed beamforming for wireless relay networks"
  Abstract: This paper deals with distributed beamforming techniques for wireless networks with half-duplex amplify-and- forward relays. Existing schemes optimize the beamforming weights based on the assumption that channel state information (CSI) is available at the relays. We propose to use adaptive beamforming based on deterministic perturbations and limited feedback (1-bit) from the destination to the relays in order to avoid CSI at the relays. Two scalable perturbation schemes are considered and practical implementation aspects are addressed. Simulation results confirm that the proposed techniques closely approach optimum performance and have satisfactory tracking properties in time-varying environments.

Connected through hyperedge HE-1 (induced by source paper "performance analysis of relay selection with enhanced dynamic decode and forward and network coding in two way relay channels"):
  with members:
    - "relay selection with network coding in two way relay channels"
    - "a simple cooperative diversity method based on network path selection"

Connected through hyperedge HE-2 (induced by source paper "deterministic differential search algorithm for distributed sensor relay networks"):
  with members:
    - "a bio inspired robust adaptive random search algorithm for distributed beamforming"
    - "a general proof of convergence for adaptive distributed beamforming schemes"
    - "a random search framework for convergence analysis of distributed beamforming with feedback"
    - "distributed transmit beamforming using feedback control"

Connected through hyperedge HE-3 (induced by source paper "energy efficient deterministic adaptive beamforming algorithms for distributed sensor relay networks"):
  with members:
    - "a bio inspired robust adaptive random search algorithm for distributed beamforming"
    - "a general proof of convergence for adaptive distributed beamforming schemes"
    - "a random search framework for convergence analysis of distributed beamforming with feedback"

Connected through hyperedge HE-4 (induced by source paper "distributed transmit beamforming using feedback control"):
  with members:
    - "collaborative beamforming for distributed wireless ad hoc sensor networks"

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)