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: "a random search framework for convergence analysis of distributed beamforming with feedback"
  Abstract: The focus of this work is on the analysis of transmit beamforming schemes with a low-rate feedback link in wireless sensor/relay networks, where nodes in the network need to implement beamforming in a distributed manner. Specifically, the problem of distributed phase alignment is considered, where neither the transmitters nor the receiver has perfect channel state information, but there is a low-rate feedback link from the receiver to the transmitters. In this setting, a framework is proposed for systematically analyzing the performance of distributed beamforming schemes. To illustrate the advantage of this framework, a simple adaptive distributed beamforming scheme that was recently proposed by Mudambai et al. is studied. Two important properties for the received signal magnitude function are derived. Using these properties and the systematic framework, it is shown that the adaptive distributed beamforming scheme converges both in probability and in mean. Furthermore, it is established that the time required for the adaptive scheme to converge in mean scales linearly with respect to the number of sensor/relay nodes.

Connected through hyperedge HE-1 (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"
    - "perturbation based distributed beamforming for wireless relay networks"
    - "distributed transmit beamforming using feedback control"

Connected through hyperedge HE-2 (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"
    - "perturbation based distributed beamforming for wireless relay networks"

Connected through hyperedge HE-3 (induced by source paper "a bio inspired robust adaptive random search algorithm for distributed beamforming"):
  with members:
    - "a general proof of convergence for adaptive distributed beamforming schemes"

Connected through hyperedge HE-4 (induced by source paper "a general proof of convergence for adaptive distributed beamforming schemes"):
  with members:
    - "distributed spatial multiplexing with 1 bit feedback"

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.DC(Distributed, Parallel, and Cluster Computing)