human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a linear network code construction for general integer connections based on the constraint satisfaction problem"):
  Abstract: The problem of finding network codes for general connections is inherently difficult in capacity constrained networks. Resource minimization for general connections with network coding is further complicated. Existing methods for identifying solutions mainly rely on highly restricted classes of network codes, and are almost all centralized. In this paper, we introduce linear network mixing coefficients for code constructions of general connections that generalize random linear network coding (RLNC) for multicast connections. For such code constructions, we pose the problem of cost minimization for the subgraph involved in the coding solution and relate this minimization to a path-based Constraint Satisfaction Problem (CSP) and an edge-based CSP. While CSPs are NP-complete in general, we present a path-based probabilistic distributed algorithm and an edge-based probabilistic distributed algorithm with almost sure convergence in finite time by applying Communication Free Learning (CFL). Our approach allows fairly general coding across flows, guarantees no greater cost than routing, and shows a possible distributed implementation. Numerical results illustrate the performance improvement of our approach over existing methods.

Includes members:
  Member "on multi source networks enumeration rate region computation and hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained linear representability of polymatroids and algorithms for computing achievability proofs in network coding")
  Member "the two unicast problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity and achievable rate regions for linear network coding over ring alphabets")
  Member "precoding based network alignment for three unicast sessions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "achievable rate regions for network coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "characteristic dependent linear rank inequalities via complementary vector spaces")
  Member "index coding an interference alignment perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "computing bounds on network capacity regions as a polytope reconstruction problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linear fractional network coding and representable discrete polymatroids")
  Member "scalar linear solvability of matroidal networks associated with representable matroids":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multicast networks solvable over every finite field")
  Member "on the index coding problem and its relation to network coding and matroid theory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient 3d road map data exchange for intelligent vehicles in vehicular fog networks")

Question: Please tell me which class the source paper 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)