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 "learning the interference graph of a wireless network"):
  Abstract: A key challenge in wireless networking is the management of interference between transmissions. Identifying which transmitters interfere with each other is a crucial first step. Complicating the task is the fact that the topology of wireless networks changes with time, and so identification may need to be performed on a regular basis. Injecting probing traffic to assess interference can lead to unacceptable overhead, and so this paper focuses on interference estimation based on passive traffic monitoring. We concentrate on networks that use the CSMA/CA protocol, although our model is more general. #R##N#We cast the task of estimating the interference environment as a graph learning problem. Nodes represent transmitters and edges represent the presence of interference between pairs of transmitters. We passively observe network traffic transmission patterns and collect information on transmission successes and failures. We establish bounds on the number of observations required to identify the interference graph reliably with high probability. #R##N#Our main results are scaling laws telling us how the number of observations must grow in terms of the total number of nodes $n$ in the network and the maximum number of interfering transmitters $d$ per node (maximum node degree). The effects of hidden terminal interference on the observation requirements are also quantified. We show that it is necessary and sufficient that the observation period grows like $d^2 \log n$, and we propose a practical algorithm that reliably identifies the graph from this length of observation. The observation requirements scale quite mildly with network size, and networks with sparse interference (small $d$) can be identified more rapidly. Computational experiments based on a realistic simulations of the traffic and protocol lend additional support to these conclusions.

Includes members:
  Member "optimizing user association and spectrum allocation in hetnets a utility perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resource allocation in green dense cellular networks complexity and algorithms")
  Member "cloud radio access network virtualizing wireless access for dense heterogeneous systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "active topology inference using network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on network tomography with network coding")
  Member "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "distributed sensor failure detection in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the compressed measurements over finite fields sparse or dense sampling")
  Member "graph constrained group testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a framework for generalized group testing with inhibitors and its potential application in neuroscience")

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)