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: "coding into a source a direct inverse rate distortion theorem"
  Abstract: Shannon proved that if we can transmit bits reliably at rates larger than the rate distortion function $R(D)$, then we can transmit this source to within a distortion $D$. We answer the converse question ``If we can transmit a source to within a distortion $D$, can we transmit bits reliably at rates less than the rate distortion function?'' in the affirmative. This can be viewed as a direct converse of the rate distortion theorem.

Connected through hyperedge HE-1 (induced by source paper "layered black box behavioral interconnection perspective and applications to the problem of communication with fidelity criteria part i i i d sources"):
  with members:
    - "a randomized covering packing duality between source coding and channel coding"
    - "information spectrum approach to the source channel separation theorem"
    - "communication over individual channels"
    - "optimality and approximate optimality of source channel separation in networks"

Connected through hyperedge HE-2 (induced by source paper "communication in the presence of a state aware adversary"):
  with members:
    - "sufficiently myopic adversaries are blind"
    - "wiretap channels with random states non causally available at the encoder"
    - "secrecy results for compound wiretap channels"
    - "upper bounds on the capacity of binary channels with causal adversaries"
    - "capacity and random coding exponents for channel coding with side information"

Connected through hyperedge HE-3 (induced by source paper "dirty paper arbitrarily varying channel with a state aware adversary"):
  with members:
    - "writing on a dirty paper in the presence of jamming"
    - "on avcs with quadratic constraints"

Connected through hyperedge HE-4 (induced by source paper "on avcs with quadratic constraints"):
  with members:
    - "relaxing the gaussian avc"
    - "communication over individual channels"

Connected through hyperedge HE-5 (induced by source paper "a universal operational theory of unicast multi user communication with fidelity criteria"):
  with members:
    - "optimality and approximate optimality of source channel separation in networks"

Connected through hyperedge HE-6 (induced by source paper "communication over individual channels"):
  with members:
    - "an achievable rate for the mimo individual channel"
    - "zero rate feedback can achieve the empirical capacity"

Connected through hyperedge HE-7 (induced by source paper "optimality and approximate optimality of source channel separation in networks"):
  with members:
    - "separation of source network coding and channel coding in wireline networks"
    - "on the separation of lossy source network coding and channel coding in wireline networks"
    - "cascade and triangular source coding with side information at the first two nodes"
    - "cascade multiterminal source coding"
    - "hybrid coding for gaussian broadcast channels with gaussian sources"
    - "approximate characterizations for the gaussian source broadcast distortion region"
    - "multicasting correlated multi source to multi sink over a network"
    - "on multistage successive refinement for wyner ziv source coding with degraded side informations"

Connected through hyperedge HE-8 (induced by source paper "architecture for communication with a fidelity criterion in unknown networks"):
  with members:
    - "optimality and approximate optimality of source channel separation in 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)