human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "optimized random deployment of energy harvesting sensors for field reconstruction in analog and digital forwarding systems"):
  Abstract: This paper examines the large-scale deployment of energy harvesting sensors for the purpose of sensing and reconstruction of a spatially correlated Gaussian random field. The sensors are powered solely by energy harvested from the environment and are deployed randomly according to a spatially non-homogeneous Poisson point process whose density depends on the energy arrival statistics at different locations. Random deployment is suitable for applications that require deployment over a wide and/or hostile area. During an observation period, each sensor takes a local sample of the random field and reports the data to the closest data-gathering node if sufficient energy is available for transmission. The realization of the random field is then reconstructed at the fusion center based on the reported sensor measurements. For the purpose of field reconstruction, the sensors should, on one hand, be more spread out over the field to gather more informative samples, but should, on the other hand, be more concentrated at locations with high energy arrival rates or large channel gains toward the closest data-gathering node. This tradeoff is exploited in the optimization of the random sensor deployment in both analog and digital forwarding systems. More specifically, given the statistics of the energy arrival at different locations and a constraint on the average number of sensors, the spatially-dependent sensor density and the energy-aware transmission policy at the sensors are determined for both cases by minimizing an upper bound on the average mean-square reconstruction error. The efficacy of the proposed schemes are demonstrated through numerical simulations.

Includes members:
  Member "spatial throughput of mobile ad hoc networks powered by energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "optimal strategies for communication and remote estimation with an energy harvesting sensor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal causal rate constrained sampling for a class of continuous markov processes")
  Member "random deployment of data collectors for serving randomly located sensors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on detection of critical events in a finite forest using randomly deployed wireless sensors")
  Member "transmission with energy harvesting nodes in fading wireless channels optimal policies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "closed form analysis of non linear age of information in status updates with an energy harvesting transmitter")
  Member "optimal packet scheduling in an energy harvesting communication system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely updates in energy harvesting two hop networks offline and online policies")
  Member "optimal energy management policies for energy harvesting sensor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "achievable rate of multi antenna wsrns with eh constraint in the presence of a jammer")
  Member "adaptive alternating minimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "partial updates losing information for freshness")

Question: Please tell me which class the source paper 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)