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: "sum networks from incidence structures construction and capacity analysis"
  Abstract: A sum-network is an instance of a network coding problem over a directed acyclic network in which each terminal node wants to compute the sum over a finite field of the information observed at all the source nodes. Many characteristics of the well-studied multiple unicast network communication problem also hold for sum-networks due to a known reduction between instances of these two problems. In this work, we describe an algorithm to construct families of sum-network instances using incidence structures. The computation capacity of several of these sum-network families is characterized. We demonstrate that unlike the multiple unicast problem, the computation capacity of sum-networks depends on the characteristic of the finite field over which the sum is computed. This dependence is very strong; we show examples of sum-networks that have a rate-1 solution over one characteristic but a rate close to zero over a different characteristic. Additionally, a sum-network can have an arbitrary different number of computation capacities for different alphabets. This is contrast to the multiple unicast problem where it is known that the capacity is independent of the network coding alphabet.

Connected through hyperedge HE-1 (induced by source paper "zero error function computation on a directed acyclic network"):
  with members:
    - "improved upper bound on the network function computing capacity"
    - "on computation rates for arithmetic sum"
    - "upper bound on function computation in directed acyclic networks"
    - "on the multiple unicast capacity of 3 source 3 terminal directed acyclic networks"
    - "distributed function computation over a rooted directed tree"
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"

Connected through hyperedge HE-2 (induced by source paper "leveraging coding techniques for speeding up distributed computing"):
  with members:
    - "low subpacketization schemes for coded caching"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "coded caching for networks with the resolvability property"
    - "fractional repetition codes with flexible repair from combinatorial designs"
    - "fundamental limits of caching"

Connected through hyperedge HE-3 (induced by source paper "coded caching schemes with reduced subpacketization from linear block codes"):
  with members:
    - "asynchronous coded caching"
    - "placement delivery array design through strong edge coloring of bipartite graphs"
    - "on the placement delivery array design for centralized coded caching scheme"
    - "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs"
    - "coded caching for networks with the resolvability property"
    - "capacity of sum networks for different message alphabets"
    - "finite length analysis of caching aided coded multicasting"
    - "improved lower bounds for coded caching"

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)