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: "channel charting locating users within the radio environment using channel state information"
  Abstract: We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC works in a fully unsupervised manner, i.e., learning is only based on channel state information (CSI) that is passively collected at a single point in space, but from multiple transmit locations in the area over time. The method then extracts channel features that characterize large-scale fading properties of the wireless channel. Finally, the channel charts are generated with tools from dimensionality reduction, manifold learning, and deep neural networks. The network element performing CC may be, for example, a multi-antenna base-station in a cellular system and the charted area in the served cell. Logical relationships related to the position and movement of a transmitter, e.g., a user equipment (UE), in the cell can then be directly deduced from comparing measured radio channel characteristics to the channel chart. The unsupervised nature of CC enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, hand-over, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base-station, without the need of information from global navigation satellite systems.

Connected through hyperedge HE-1 (induced by source paper "mse optimal neural network initialization via layer fusion"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "deep learning assisted csi estimation for joint urllc and embb resource allocation"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "embb urllc resource slicing a risk sensitive approach"
    - "wireless network intelligence at the edge"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "adam a method for stochastic optimization"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-3 (induced by source paper "siamese neural networks for wireless positioning and channel charting"):
  with members:
    - "fully convolutional siamese networks for object tracking"
    - "a siamese long short term memory architecture for human re identification"
    - "learning to compare image patches via convolutional neural networks"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-4 (induced by source paper "on tracking the physicality of wi fi a subspace approach"):
  with members:
    - "securing on body iot devices by exploiting creeping wave propagation"
    - "eigenvalue dynamics of a central wishart matrix with application to mimo 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)