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: "design of spectrally shaped binary sequences via randomized convex relaxation"
  Abstract: Wideband communication receivers often deal with the problems of detecting weak signals from distant sources received together with strong nearby interferers. When the techniques of random modulation are used in communication system receivers, one can design a spectrally shaped sequence that mitigates interferer bands while preserving message bands. Common implementation constraints require sequence quantization, which turns the design problem formulation to an integer optimization problem solved using a semidefinite program on a matrix that is restricted to have rank one. Common approximation schemes for this problem are not amenable due to the distortion to the spectrum caused by the required quantization. We propose a method that leverages a randomized projection and quantization of the solution of a semidefinite program, an approach that has been previously used for related integer programs. We provide a theoretical and numerical analysis on the feasibility and quality of the approximation provided by the proposed approach. Furthermore, numerical simulations show that our proposed approach returns the same sequence as an exhaustive search (when feasible), showcasing its accuracy and efficiency. Furthermore, our proposed method succeeds in finding suitable spectrally shaped sequences for cases where exhaustive search is not feasible, achieving better performance than existing alternatives.

Connected through hyperedge HE-1 (induced by source paper "mimo beampattern and waveform design with low resolution dacs"):
  with members:
    - "ieee 802 11ad based radar an approach to joint vehicular communication radar system"
    - "massive mimo downlink 1 bit precoding with linear programming for psk signaling"
    - "quantized precoding for massive mu mimo"
    - "bilinear generalized approximate message passing part i derivation"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-2 (induced by source paper "binary sequence set design for interferer rejection in multi branch modulation"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

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)