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: "nomographic functions efficient computation in clustered gaussian sensor networks"
  Abstract: In this paper, a clustered wireless sensor network is considered that is modeled as a set of coupled Gaussian multiple-access channels. The objective of the network is not to reconstruct individual sensor readings at designated fusion centers but rather to reliably compute some functions thereof. Our particular attention is on real-valued functions that can be represented as a post-processed sum of pre-processed sensor readings. Such functions are called nomographic functions and their special structure permits the utilization of the interference property of the Gaussian multiple-access channel to reliably compute many linear and nonlinear functions at significantly higher rates than those achievable with standard schemes that combat interference. Motivated by this observation, a computation scheme is proposed that combines a suitable data pre- and post-processing strategy with a nested lattice code designed to protect the sum of pre-processed sensor readings against the channel noise. After analyzing its computation rate performance, it is shown that at the cost of a reduced rate, the scheme can be extended to compute every continuous function of the sensor readings in a finite succession of steps, where in each step a different nomographic function is computed. This demonstrates the fundamental role of nomographic representations.

Connected through hyperedge HE-1 (induced by source paper "computing and communicating functions in disorganized wireless networks"):
  with members:
    - "over the air function computation in sensor networks"
    - "upper bound on function computation in directed acyclic networks"
    - "computation over gaussian networks with orthogonal components"
    - "computing linear functions by linear coding over networks"
    - "duty cycle aware minimum energy multicasting in wireless sensor networks"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-2 (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"
    - "robust analog function computation via wireless multiple access channels"

Connected through hyperedge HE-3 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow"):
  with members:
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "over the air computation via intelligent reflecting surfaces"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "scheduling policies for federated learning in wireless networks"
    - "the roadmap to 6g ai empowered wireless networks"
    - "blind demixing for low latency communication"
    - "nonconvex demixing from bilinear measurements"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"

Connected through hyperedge HE-4 (induced by source paper "on secure computation over the binary modulo 2 adder multiple access wiretap channel"):
  with members:
    - "two shannon type problems on secure multi party computations"
    - "how to securely compute the modulo two sum of binary sources"
    - "distributed function computation with confidentiality"
    - "computation over mismatched channels"
    - "robust analog function computation via wireless multiple access channels"
    - "when is a function securely computable"
    - "the gaussian multiple access wire tap channel"

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)