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: "robust analog function computation via wireless multiple access channels"
  Abstract: Various wireless sensor network applications involve the computation of a pre-defined function of the measurements without the need for reconstructing each individual sensor reading. Widely-considered examples of such functions include the arithmetic mean and the maximum value. Standard approaches to the computation problem separate computation from communication: quantized sensor readings are transmitted interference-free to a fusion center that reconstructs each sensor reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual sensor readings is not necessary for the fusion center to compute a function of them. In particular, if the mathematical structure of the wireless channel is suitably matched (in some sense) to the function, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. Therefore, in this paper a practically relevant analog computation scheme is proposed that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. After analyzing the asymptotic properties of the estimation error, numerical simulations are presented to show the potential for huge performance gains when compared with time-division multiple-access based computation schemes.

Connected through hyperedge HE-1 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "securing distributed function approximation via coding for continuous compound channels"):
  with members:
    - "distributed approximation of functions over fast fading channels with applications to distributed learning and the max consensus problem"
    - "resolvability on continuous alphabets"
    - "energy efficient classification for anomaly detection the wireless channel as a helper"
    - "renyi divergence and kullback leibler divergence"
    - "strong secrecy from channel resolvability"

Connected through hyperedge HE-3 (induced by source paper "machine learning at the wireless edge distributed stochastic gradient descent over the air"):
  with members:
    - "computation scheduling for distributed machine learning with straggling workers"
    - "hierarchical coded computation"
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "sparcml high performance sparse communication for machine learning"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"

Connected through hyperedge HE-4 (induced by source paper "over the air computation systems optimization analysis and scaling laws"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "wireless networked control systems with coding free data transmission for industrial iot"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "optimal power control for over the air computation in fading channels"
    - "exploiting the superposition property of wireless communication for max consensus problems in multi agent systems"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "inference from randomized transmissions by many backscatter sensors"
    - "over the air function computation in sensor networks"

Connected through hyperedge HE-5 (induced by source paper "distributed approximation of functions over fast fading channels with applications to distributed learning and the max consensus problem"):
  with members:
    - "a scalable max consensus protocol for noisy ultra dense networks"
    - "energy efficient classification for anomaly detection the wireless channel as a helper"
    - "nomographic functions efficient computation in clustered gaussian sensor networks"

Connected through hyperedge HE-6 (induced by source paper "a scalable max consensus protocol for noisy ultra dense networks"):
  with members:
    - "exploiting the superposition property of wireless communication for max consensus problems in multi agent systems"
    - "5g ultra dense cellular networks"

Connected through hyperedge HE-7 (induced by source paper "distributed differentially private computation of functions with correlated noise"):
  with members:
    - "a simple algorithm for approximation by nomographic functions"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-8 (induced by source paper "federated learning via over the air computation"):
  with members:
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "federated learning with non iid data"

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)