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: "order optimal consensus through randomized path averaging"
  Abstract: Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust message-passing schemes for distributed information processing over networks. However for many topologies that are realistic for wireless ad-hoc and sensor networks (like grids and random geometric graphs), the standard nearest-neighbor gossip converges as slowly as flooding ($O(n^2)$ messages). #R##N#A recently proposed algorithm called geographic gossip improves gossip efficiency by a $\sqrt{n}$ factor, by exploiting geographic information to enable multi-hop long distance communications. In this paper we prove that a variation of geographic gossip that averages along routed paths, improves efficiency by an additional $\sqrt{n}$ factor and is order optimal ($O(n)$ messages) for grids and random geometric graphs. #R##N#We develop a general technique (travel agency method) based on Markov chain mixing time inequalities, which can give bounds on the performance of randomized message-passing algorithms operating over various graph topologies.

Connected through hyperedge HE-1 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks"):
  with members:
    - "a parallel algorithm for generating a random graph with a prescribed degree sequence"
    - "multi scale spectrum sensing in small cell mm wave cognitive wireless networks"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "investigation of prediction accuracy sensitivity and parameter stability of large scale propagation path loss models for 5g wireless communications"
    - "gains of restricted secondary licensing in millimeter wave cellular systems"
    - "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"

Connected through hyperedge HE-2 (induced by source paper "multi scale spectrum sensing in 5g cognitive networks"):
  with members:
    - "multi scale spectrum sensing in small cell mm wave cognitive wireless networks"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "gains of restricted secondary licensing in millimeter wave cellular systems"
    - "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"

Connected through hyperedge HE-3 (induced by source paper "multi scale spectrum sensing in small cell mm wave cognitive wireless networks"):
  with members:
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "toward resource optimal consensus over the wireless medium"

Connected through hyperedge HE-4 (induced by source paper "finite time convergent gossiping"):
  with members:
    - "consensus over random graph processes network borel cantelli lemmas for almost sure convergence"
    - "finite time consensus using stochastic matrices with positive diagonals"
    - "the evolution of beliefs over signed social networks"
    - "analysis of sum weight like algorithms for averaging in wireless sensor networks"
    - "opinion fluctuations and disagreement in social networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "fast distributed algorithms for computing separable functions"

Connected through hyperedge HE-5 (induced by source paper "a new perspective on randomized gossip algorithms"):
  with members:
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "gossip algorithms for distributed signal processing"
    - "geographic gossip efficient averaging for sensor networks"

Connected through hyperedge HE-6 (induced by source paper "consensus based modelling using distributed feature construction"):
  with members:
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "on decentralized estimation with active queries"):
  with members:
    - "collaborative 20 questions for target localization"
    - "toward resource optimal consensus over the wireless medium"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "active testing for face detection and localization"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-8 (induced by source paper "toward resource optimal consensus over the wireless medium"):
  with members:
    - "convergence speed of the consensus algorithm with interference and sparse long range connectivity"
    - "capacity scaling of wireless ad hoc networks shannon meets maxwell"
    - "multiscale gossip for efficient decentralized averaging in wireless packet networks"
    - "geographic gossip efficient averaging for sensor networks"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

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)