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: "proactive resource allocation turning predictable behavior into spectral gain"
  Abstract: This paper introduces the novel concept of proactive resource allocation in which the predictability of user behavior is exploited to balance the wireless traffic over time, and hence, significantly reduce the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which the smart wireless devices are assumed to predict the arrival of new requests and submit them to the network T time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases linearly with the prediction duration T. This model is then generalized to incorporate the effect of prediction errors and the randomness in the prediction lookahead time T. Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by the primary users results in more spectral opportunities for the secondary users at a marginal, or no, cost in the primary network outage. Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive (non-causal) wireless networking framework.

Connected through hyperedge HE-1 (induced by source paper "towards jointly optimal placement and delivery to code or not to code in wireless caching networks"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "optimization of heterogeneous coded caching"
    - "structural properties of uncoded placement optimization for coded delivery"
    - "decentralized coded caching attains order optimal memory rate tradeoff"
    - "fundamental limits of caching"
    - "economics of wifi offloading trading delay for cellular capacity"
    - "proactive resource allocation harnessing the diversity and multicast gains"

Connected through hyperedge HE-2 (induced by source paper "degrees of freedom of interference networks with transmitter side caches"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "decentralized coded caching in wireless networks trade off between storage and latency"
    - "fundamental tradeoff between storage and latency in cache aided wireless interference networks"
    - "degrees of freedom of cache aided wireless interference networks"
    - "decentralized coded caching with distinct cache capacities"
    - "cloud and cache aided wireless networks fundamental latency trade offs"
    - "cache aided wireless networks tradeoffs between storage and latency"

Connected through hyperedge HE-3 (induced by source paper "proactive data download and user demand shaping for data networks"):
  with members:
    - "proactive resource allocation harnessing the diversity and multicast gains"

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)