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 "performance bounds for remote estimation under energy harvesting constraints"):
  Abstract: Remote estimation with an energy harvesting sensor with a limited data and energy buffer is considered. The sensor node observes an unknown Gaussian field and communicates its observations to a remote fusion center using the energy it harvested. The fusion center employs minimum mean-square error (MMSE) estimation to reconstruct the unknown field. The distortion minimization problem under the online scheme, where the sensor has access to only the statistical information for the future energy packets is considered. We provide performance bounds on the achievable distortion under a slotted block transmission scheme, where at each transmission time slot, the data and the energy buffer are completely emptied. Our bounds provide insights to the trade-offs between the buffer sizes, the statistical properties of the energy harvesting process and the achievable distortion. In particular, these trade-offs illustrate the insensitivity of the performance to the buffer sizes for signals with low degree of freedom and suggest performance improvements with increasing buffer size for signals with relatively higher degree of freedom. Depending only on the mean, variance and finite support of the energy arrival process, these results provide practical insights for the battery and buffer sizes for deployment in future energy harvesting wireless sensing systems.

Includes members:
  Member "linear transmission of composite gaussian measurements over a fading channel under delay constraints":
    also connected through other hyperedges:
      (none)
  Member "source channel coding under energy delay and buffer constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging linear quadratic regulator cost and energy consumption for ultra reliable and low latency iot control systems")
  Member "near optimal energy control and approximate capacity of energy harvesting communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a maximin optimal online power control policy for energy harvesting communications")
  Member "simultaneous information and power transfer for broadband wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transmission delay minimization in wireless powered communication systems")
  Member "a learning theoretic approach to energy harvesting communication system optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "support recovery with sparsely sampled free random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "optimal strategies for communication and remote estimation with an energy harvesting sensor":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal causal rate constrained sampling for a class of continuous markov processes")
  Member "unitary precoding and basis dependency of mmse performance for gaussian erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lattice erasure codes of low rank with noise margins")

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)