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: "on the effects of subpacketization in content centric mobile networks"
  Abstract: A large-scale content-centric mobile ad hoc network employing subpacketization is studied in which each mobile node having finite-size cache moves according to the reshuffling mobility model and requests a content object from the library independently at random according to the Zipf popularity distribution. Instead of assuming that one content object is transferred in a single time slot, we consider a more challenging scenario where the size of each content object is considerably large and thus only a subpacket of a file can be delivered during one time slot, which is motivated by a fast mobility scenario. Under our mobility model, we consider a single-hop-based content delivery and characterize the fundamental trade-offs between throughput and delay. The order-optimal throughput-delay trade-off is analyzed by presenting the following two content reception strategies: the sequential reception for uncoded caching and the random reception for maximum distance separable (MDS)-coded caching. We also perform numerical evaluation to validate our analytical results. In particular, we conduct performance comparisons between the uncoded caching and the MDS-coded caching strategies by identifying the regimes in which the performance difference between the two caching strategies becomes prominent with respect to system parameters such as the Zipf exponent and the number of subpackets. In addition, we extend our study to the random walk mobility scenario and show that our main results are essentially the same as those in the reshuffling mobility model.

Connected through hyperedge HE-1 (induced by source paper "a personalized preference learning framework for caching in mobile networks"):
  with members:
    - "caching at base stations with heterogeneous user demands and spatial locality"
    - "user preference learning based edge caching for fog radio access network"
    - "how to cache in mobile hybrid iot networks"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "pricing and resource allocation via game theory for a small cell video caching system"
    - "information theoretic caching the multi user case"
    - "caching incentive design in wireless d2d networks a stackelberg game approach"
    - "a learning based approach to caching in heterogenous small cell networks"

Connected through hyperedge HE-2 (induced by source paper "the role of caching in future communication systems and networks"):
  with members:
    - "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "on coding for cache aided delivery of dynamic correlated content"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "updating content in cache aided coded multicast"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "a reinforcement learning approach to proactive caching in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "network decomposed hierarchical cooperation in ad hoc networks with social relationships"):
  with members:
    - "leveraging social communities for optimizing cellular device to device communications"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "parallel opportunistic routing in wireless networks"
    - "asymptotic laws for joint content replication and delivery in wireless networks"
    - "improved capacity scaling in wireless networks with infrastructure"
    - "capacity scaling of wireless ad hoc networks shannon meets maxwell"
    - "on capacity scaling in arbitrary wireless networks"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc 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)