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: "age of information in multiple sensing"
  Abstract: Having timely and fresh knowledge about the current state of information sources is critical in a variety of applications. In particular, a status update may arrive at the destination much later than its generation time due to processing and communication delays. The freshness of the status update at the destination is captured by the notion of age of information. In this study, we first analyze a network with a single source, $n$ servers, and the monitor (destination). The servers independently sense the source of information and send the status update to the monitor. We then extend our result to multiple independent sources of information in the presence of $n$ servers. We assume that updates arrive at the servers according to Poisson random processes. Each server sends its update to the monitor through a direct link, which is modeled as a queue. The service time to transmit an update is considered to be an exponential random variable. We examine both homogeneous and heterogeneous service and arrival rates for the single-source case, and only homogeneous arrival and service rates for the multiple sources case. We derive a closed-form expression for the average age of information under a last-come-first-serve (LCFS) queue for a single source and arbitrary $n$ homogeneous servers. For $n=2,3$, we derive the explicit average age of information for arbitrary sources and homogeneous servers, and for a single source and heterogeneous servers. For $n=2$ we find the optimal arrival rates given a fixed sum arrival rate and service rates.

Connected through hyperedge HE-1 (induced by source paper "on the age of information in internet of things systems with correlated devices"):
  with members:
    - "age of information analysis for dynamic spectrum sharing"
    - "minimizing the age of information of cognitive radio based iot systems under a collision constraint"
    - "sampling for remote estimation through queues age of information and beyond"
    - "a reinforcement learning framework for optimizing age of information in rf powered communication systems"
    - "age of information in multiple sensing of a single source"
    - "status from a random field how densely should one update"
    - "minimizing the age of information in a csma environment"
    - "scheduling to minimize age of information in multi state time varying networks with power constraints"

Connected through hyperedge HE-2 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage"):
  with members:
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-3 (induced by source paper "a new codebook design for analog beamforming in millimeter wave communication"):
  with members:
    - "investigating robustness and interpretability of link prediction via adversarial modifications"
    - "partition pruning parallelization aware pruning for deep neural networks"
    - "on the age of information in erasure channels with feedback"
    - "generating user friendly explanations for loan denials using gans"
    - "hybrid precoder and combiner design with low resolution phase shifters in mmwave mimo systems"
    - "embedding multimodal relational data for knowledge base completion"
    - "on the performance of mrc receiver with unknown timing mismatch a large scale analysis"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave 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)