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: "grant free massive noma outage probability and throughput"
  Abstract: In this paper, we consider a massive uncoordinated non-orthogonal multiple access (NOMA) scheme where devices have strict latency requirements and no retransmission opportunities are available. Each device chooses a pilot sequence from a predetermined set as its signature and transmits its selected pilot and data simultaneously. A collision occurs when two or more devices choose the same pilot sequence. Collisions are treated as interference to the remaining received signals. We consider successive joint decoding (SJD) and successive interference cancellation (SIC) under a Rayleigh fading and path loss channel model. We first derive the expression for the outage probability for the case where devices transmit at the same fixed rate. Then, we derive the expression for the maximum achievable throughput for the case where devices transmit with rateless codes. Thus, their code rate is adaptive to the system conditions, i.e., load, received powers, and interference. Numerical results verify the accuracy of our analytical expressions. For low data rate transmissions, results show that SIC performs close to that of SJD in terms of outage probability for packet arrival rates up to 10 packets per slot. However, SJD can achieve almost double the throughput of SIC and is, thus, far more superior.

Connected through hyperedge HE-1 (induced by source paper "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"):
  with members:
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "random access schemes in wireless systems with correlated user activity"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"

Connected through hyperedge HE-2 (induced by source paper "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach"):
  with members:
    - "massive multiple access based on superposition raptor codes for cellular m2m communications"
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "data aggregation and packet bundling of uplink small packets for monitoring applications in lte"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "energy efficient scheduling and grouping for machine type communications over cellular networks"
    - "a delay optimal mac and packet scheduler for heterogeneous m2m uplink"
    - "massive machine type communication with data aggregation and resource scheduling"
    - "maximizing the link throughput between smart meters and aggregators as secondary users under power and outage constraints"

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)