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: "a framework for optimizing multi cell noma delivering demand with less resource"
  Abstract: Non-orthogonal multiple access (NOMA) allows multiple users to simultaneously access the same time-frequency resource by using superposition coding and successive interference cancellation (SIC). Thus far, most papers on NOMA have focused on performance gain for one or sometimes two base stations. In this paper, we study multi-cell NOMA and provide a general framework for user clustering and power allocation, taking into account inter-cell interference, for optimizing resource allocation of NOMA in multi-cell networks of arbitrary topology. We provide a series of theoretical analysis, to algorithmically enable optimization approaches. The resulting algorithmic notion is very general. Namely, we prove that for any performance metric that monotonically increases in the cells' resource consumption, we have convergence guarantee for global optimum. We apply the framework with its algorithmic concept to a multi-cell scenario to demonstrate the gain of NOMA in achieving significantly higher efficiency.

Connected through hyperedge HE-1 (induced by source paper "a note on decoding order in optimizing multi cell noma"):
  with members:
    - "a robust machine learning method for cell load approximation in wireless networks"
    - "resource optimization with load coupling in multi cell noma"
    - "on optimal power allocation for downlink non orthogonal multiple access systems"
    - "energy aware wireless relay selection in load coupled ofdma cellular networks"
    - "load optimization with user association in cooperative and load coupled lte networks"
    - "dynamic user clustering and power allocation for uplink and downlink non orthogonal multiple access noma systems"
    - "elementary properties of positive concave mappings with applications to network planning and optimization"
    - "power and channel allocation for non orthogonal multiple access in 5g systems tractability and computation"

Connected through hyperedge HE-2 (induced by source paper "resource optimization with load coupling in multi cell noma"):
  with members:
    - "a robust machine learning method for cell load approximation in wireless networks"
    - "on optimal power allocation for downlink non orthogonal multiple access systems"
    - "energy aware wireless relay selection in load coupled ofdma cellular networks"
    - "sub channel assignment power allocation and user scheduling for non orthogonal multiple access networks"
    - "load optimization with user association in cooperative and load coupled lte networks"
    - "elementary properties of positive concave mappings with applications to network planning and optimization"
    - "power and channel allocation for non orthogonal multiple access in 5g systems tractability and computation"
    - "energy efficiency of distributed signal processing in wireless networks a cross layer analysis"

Connected through hyperedge HE-3 (induced by source paper "resource optimization in multi cell noma"):
  with members:
    - "on optimal power allocation for downlink non orthogonal multiple access systems"
    - "energy aware wireless relay selection in load coupled ofdma cellular networks"
    - "load optimization with user association in cooperative and load coupled lte networks"
    - "elementary properties of positive concave mappings with applications to network planning and optimization"
    - "energy efficiency of distributed signal processing in wireless networks a cross layer analysis"
    - "non orthogonal multiple access in multi cell networks theory performance and practical challenges"
    - "power and load coupling in cellular networks for energy optimization"
    - "optimal cell clustering and activation for energy saving in load coupled wireless 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)