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: "integrated semi definite relaxation receiver for ldpc coded mimo systems"
  Abstract: Semi-definite relaxation (SDR) detector has been demonstrated to be successful in approaching maximum likelihood (ML) performance while the time complexity is only polynomial. We propose a new receiver jointly utilizing the forward error correction (FEC) code information in the SDR detection process. Strengthened by code constraints, the joint SDR detector substantially improves the overall receiver performance. For further performance boost, the ML-SDR detector is adapted to MAP-SDR detector by incorporating a priori information in the cost function. Under turbo principle, MAP-SDR detector takes in soft information from decoder and outputs extrinsic information with much improved reliability. We also propose a simplified SDR turbo receiver that solves only one SDR per codeword instead of solving multiple SDRs in the iterative turbo processing. This scheme significantly reduces the time complexity of SDR turbo receiver, while the error performance remains similar as before. In fact, our simplified scheme is generic and it can be applied to any list-based iterative receivers.

Connected through hyperedge HE-1 (induced by source paper "unified receiver design in wireless relay networks using mixed integer programming techniques"):
  with members:
    - "joint turbo receiver for ldpc coded mimo systems based on semi definite relaxation"
    - "non iterative joint detection decoding receiver for ldpc coded mimo systems based on sdr"
    - "semidefinite relaxation based blind equalization using constant modulus criterion"
    - "phase noise compensation using limited reference symbols in 3gpp lte downlink"

Connected through hyperedge HE-2 (induced by source paper "semidefinite relaxation based blind equalization using constant modulus criterion"):
  with members:
    - "joint turbo receiver for ldpc coded mimo systems based on semi definite relaxation"
    - "non iterative joint detection decoding receiver for ldpc coded mimo systems based on sdr"

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)