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: "computing linear functions by linear coding over networks"
  Abstract: We consider the scenario in which a set of sources generate messages in a network and a receiver node demands an arbitrary linear function of these messages. We formulate an algebraic test to determine whether an arbitrary network can compute linear functions using linear codes. We identify a class of linear functions that can be computed using linear codes in every network that satisfies a natural cut-based condition. Conversely, for another class of linear functions, we show that the cut-based condition does not guarantee the existence of a linear coding solution. For linear functions over the binary field, the two classes are complements of each other.

Connected through hyperedge HE-1 (induced by source paper "how to distribute computation in networks"):
  with members:
    - "on the derivative imbalance and ambiguity of functions"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "comments on cut set bounds on network function computation"
    - "optimal function computation in directed and undirected graphs"

Connected through hyperedge HE-2 (induced by source paper "computing and communicating functions in disorganized wireless networks"):
  with members:
    - "over the air function computation in sensor networks"
    - "upper bound on function computation in directed acyclic networks"
    - "nomographic functions efficient computation in clustered gaussian sensor networks"
    - "computation over gaussian networks with orthogonal components"
    - "duty cycle aware minimum energy multicasting in wireless sensor networks"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-3 (induced by source paper "improved upper bound on the network function computing capacity"):
  with members:
    - "upper bound on function computation in directed acyclic networks"
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"
    - "linear codes target function classes and network computing capacity"
    - "on network functional compression"
    - "communicating the sum of sources over a network"

Connected through hyperedge HE-4 (induced by source paper "condense a reconfigurable knowledge acquisition architecture for future 5g iot"):
  with members:
    - "network coding as a service"
    - "photonic delay systems as machine learning implementations"
    - "software defined networking a comprehensive survey"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "distributed function computation over a rooted directed tree"
    - "linear coding schemes for the distributed computation of subspaces"
    - "internet of things iot a vision architectural elements and future directions"
    - "on network coding for sum networks"

Connected through hyperedge HE-5 (induced by source paper "over the air function computation in sensor networks"):
  with members:
    - "robust analog function computation via wireless multiple access channels"
    - "airsync enabling distributed multiuser mimo with full spatial multiplexing"
    - "communicating the sum of sources over a network"

Connected through hyperedge HE-6 (induced by source paper "graph codes for distributed instant message collection in an arbitrary noisy broadcast network"):
  with members:
    - "sum networks from undirected graphs construction and capacity analysis"
    - "energy efficient distributed coding for data collection in a noisy sparse network"
    - "computing linear transformations with unreliable components"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "computation over gaussian networks with orthogonal components"
    - "network coding for computing cut set bounds"
    - "towards a queueing based framework for in network function computation"
    - "optimal function computation in directed and undirected graphs"

Connected through hyperedge HE-7 (induced by source paper "comments on cut set bounds on network function computation"):
  with members:
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"
    - "linear codes target function classes and network computing capacity"
    - "communicating the sum of sources over a network"

Connected through hyperedge HE-8 (induced by source paper "upper bound on function computation in directed acyclic networks"):
  with members:
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"
    - "communicating the sum of sources over a network"

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)