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: "user association and interference management in massive mimo hetnets"
  Abstract: Two key traits of 5G cellular networks are much higher base station (BS) densities - especially in the case of low-power BSs - and the use of massive MIMO at these BSs. This paper explores how massive MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges arising from adding small cells. We consider two interference management approaches: joint transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without requiring channel state information exchanges among cooperating BSs, and resource blanking, where some macro BS resources are left blank to reduce the interference in the small cell downlink. A key advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution. Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without interference management.

Connected through hyperedge HE-1 (induced by source paper "efficient c ran random access for iot devices learning links via recommendation systems"):
  with members:
    - "compressive channel estimation and multi user detection in c ran"
    - "a novel alternative to cloud ran for throughput densification coded pilots and fast user packet scheduling at remote radio heads"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "random access protocols for massive mimo"
    - "directional training and fast sector based processing schemes for mmwave channels"
    - "optimal user cell association for massive mimo wireless networks"
    - "analysis of blockage effects on urban cellular networks"

Connected through hyperedge HE-2 (induced by source paper "user association and load balancing for massive mimo through deep learning"):
  with members:
    - "massive mimo has unlimited capacity"
    - "joint power allocation and user association optimization for massive mimo systems"
    - "wireless power transfer in massive mimo aided hetnets with user association"
    - "massive mimo with multi cell mmse processing exploiting all pilots for interference suppression"
    - "adam a method for stochastic optimization"
    - "optimal user cell association for massive mimo wireless networks"

Connected through hyperedge HE-3 (induced by source paper "non orthogonal multiple access in large scale heterogeneous networks"):
  with members:
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "modulation and multiple access for 5g networks"
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "enhancing the physical layer security of non orthogonal multiple access in large scale networks"
    - "cooperative non orthogonal multiple access with simultaneous wireless information and power transfer"
    - "the application of mimo to non orthogonal multiple access"
    - "fairness for non orthogonal multiple access in 5g systems"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"

Connected through hyperedge HE-4 (induced by source paper "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"):
  with members:
    - "backhaul aware user association and resource allocation for energy constrained hetnets"
    - "distributed association and relaying with fairness in millimeter wave networks"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "efficient scheduling and power allocation for d2d assisted wireless caching networks"
    - "mmwave massive mimo based wireless backhaul for the 5g ultra dense network"
    - "matching theory for backhaul management in small cell networks with mmwave capabilities"
    - "downlink performance of cellular systems with base station sleeping user association and scheduling"
    - "an overview of load balancing in hetnets old myths and open problems"

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)