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: "joint power allocation and user association optimization for massive mimo systems"
  Abstract: This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem which maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.

Connected through hyperedge HE-1 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks"):
  with members:
    - "scalable cell free massive mimo systems"
    - "ubiquitous cell free massive mimo communications"
    - "a speculative study on 6g"
    - "optimal design of energy efficient cell free massive mimo joint power allocation and load balancing"
    - "making cell free massive mimo competitive with mmse processing and centralized implementation"
    - "downlink spectral efficiency of cell free massive mimo with full pilot zero forcing"
    - "distributed power control in downlink cellular massive mimo systems"
    - "energy efficiency maximization for c rans discrete monotonic optimization penalty and ell _ 0 approximation methods"

Connected through hyperedge HE-2 (induced by source paper "performance of multi cell massive mimo systems with interference decoding"):
  with members:
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "massive mimo has unlimited capacity"
    - "gaussian multiple access via compute and forward"
    - "uplink interference reduction in large scale antenna systems"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "optimal achievable rates for interference networks with random codes"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-3 (induced by source paper "power control in cellular massive mimo with varying user activity a deep learning solution"):
  with members:
    - "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"
    - "optimized power control for massive mimo with underlaid d2d communications"
    - "joint transmit and circuit power minimization in massive mimo with downlink sinr constraints when to turn on massive mimo"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "learning to optimize training deep neural networks for interference management"
    - "model aided wireless artificial intelligence embedding expert knowledge in deep neural networks towards wireless systems optimization"
    - "residual dense network for image super resolution"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-4 (induced by source paper "rate analysis of cell free massive mimo noma with three linear precoders"):
  with members:
    - "what role can noma play in massive mimo"
    - "ubiquitous cell free massive mimo communications"
    - "interplay between noma and other emerging technologies a survey"
    - "downlink spectral efficiency of cell free massive mimo with full pilot zero forcing"
    - "signal processing for mimo noma present and future challenges"
    - "on the total energy efficiency of cell free massive mimo"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "channel hardening and favorable propagation in cell free massive mimo with stochastic geometry"

Connected through hyperedge HE-5 (induced by source paper "optimal design of energy efficient cell free massive mimo joint power allocation and load balancing"):
  with members:
    - "ubiquitous cell free massive mimo communications"
    - "making cell free massive mimo competitive with mmse processing and centralized implementation"
    - "downlink spectral efficiency of cell free massive mimo with full pilot zero forcing"
    - "enhanced max min sinr for uplink cell free massive mimo systems"
    - "performance of cell free massive mimo systems with mmse and lsfd receivers"
    - "on the total energy efficiency of cell free massive mimo"
    - "energy efficiency in cell free massive mimo with zero forcing precoding design"
    - "cell free massive mimo versus small cells"

Connected through hyperedge HE-6 (induced by source paper "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"):
  with members:
    - "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "hybrid beamforming for massive mimo a survey"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "hierarchical interference mitigation for massive mimo cellular networks"
    - "low complexity hybrid precoding in massive multiuser mimo systems"

Connected through hyperedge HE-7 (induced by source paper "user association and load balancing for massive mimo through deep learning"):
  with members:
    - "massive mimo has unlimited capacity"
    - "wireless power transfer in massive mimo aided hetnets with user association"
    - "massive mimo with multi cell mmse processing exploiting all pilots for interference suppression"
    - "user association and interference management in massive mimo hetnets"
    - "adam a method for stochastic optimization"
    - "optimal user cell association for massive mimo wireless networks"

Connected through hyperedge HE-8 (induced by source paper "distributed power control in downlink cellular massive mimo systems"):
  with members:
    - "uplink interference reduction in large scale antenna systems"
    - "joint pilot design and uplink power allocation in multi cell massive mimo systems"
    - "robust chance constrained optimization for power efficient and secure swipt systems"
    - "cell free massive mimo uniformly great service for everyone"
    - "outage constrained robust transmit optimization for multiuser miso downlinks tractable approximations by conic optimization"

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)