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: "study on a low complexity ecg compression scheme with multiple sensors"
  Abstract: The industry of wearable remote health monitoring system keeps growing. In the diagnosis of cardiovascular disease, Electrocardiography~(ECG) waveform is one of the major tools which is thus widely taken as the monitoring objective. For the purpose of reducing bit expenditure in the monitoring systems, we study the compression of ECG signal and propose a new compressor in low complexity. Different from the traditional ECG compressors, most of which are built on a single sensor, our compression scheme is based on multiple ECG sensors. The multi-sensor based compression scheme is able to provide more accurate sensing results. Besides the investigation into the structure of the compressor, we also jointly optimize the period and the bit number per sample in the transmission of ECG signal. Experiments are performed on records in MIT-BIH Arrhythmis database and European ST-T database. Experimental results show that our method outperforms conventional ones with respect to ECG reconstruction accuracy at the same bit rate consumption

Connected through hyperedge HE-1 (induced by source paper "on channel state feedback model and overhead in theoretical and practical views"):
  with members:
    - "joint relay and jammer selection improves the physical layer security in the face of csi feedback delays"
    - "a differential feedback scheme exploiting the temporal and spectral correlation"
    - "interference alignment with quantized grassmannian feedback in the k user constant mimo interference channel"
    - "on the minimum differential feedback for time correlated mimo rayleigh block fading channels"

Connected through hyperedge HE-2 (induced by source paper "ecg signal compression and optimization in remote monitoring networks"):
  with members:
    - "exploiting prior knowledge in compressed sensing wireless ecg systems"

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)