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: "principal inertia components and applications"
  Abstract: We explore properties and applications of the Principal Inertia Components (PICs) between two discrete random variables $X$ and $Y$. The PICs lie in the intersection of information and estimation theory, and provide a fine-grained decomposition of the dependence between $X$ and $Y$. Moreover, the PICs describe which functions of $X$ can or cannot be reliably inferred (in terms of MMSE) given an observation of $Y$. We demonstrate that the PICs play an important role in information theory, and they can be used to characterize information-theoretic limits of certain estimation problems. In privacy settings, we prove that the PICs are related to fundamental limits of perfect privacy.

Connected through hyperedge HE-1 (induced by source paper "task based quantization with application to mimo receivers"):
  with members:
    - "dynamic metasurface antennas for mimo ofdm receivers with bit limited adcs"
    - "dynamic metasurface antennas for uplink massive mimo systems"
    - "hardware limited task based quantization"
    - "on the spectral efficiency of noncooperative uplink massive mimo systems"
    - "spectral efficiency of mixed adc massive mimo"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "the privacy funnel from the viewpoint of local differential privacy"):
  with members:
    - "encode shuffle analyze privacy revisited formalizations and empirical evaluation"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "information theoretic metrics for local differential privacy protocols"
    - "on perfect privacy and maximal correlation"
    - "differential privacy as a mutual information constraint"
    - "extremal mechanisms for local differential privacy"
    - "what can we learn privately"

Connected through hyperedge HE-3 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "opening the black box of deep neural networks via information"
    - "on the emergence of invariance and disentangling in deep representations"

Connected through hyperedge HE-4 (induced by source paper "estimation efficiency under privacy constraints"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "generalizing bottleneck problems"
    - "privacy aware guessing efficiency"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "privacy aware mmse estimation"
    - "notes on information theoretic privacy"
    - "information extraction under privacy constraints"

Connected through hyperedge HE-5 (induced by source paper "on the robustness of information theoretic privacy measures and mechanisms"):
  with members:
    - "data disclosure under perfect sample privacy"
    - "privacy against brute force inference attacks"
    - "estimation efficiency under privacy constraints"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "the utility cost of robust privacy guarantees"
    - "privacy under hard distortion constraints"
    - "a tunable measure for information leakage"

Connected through hyperedge HE-6 (induced by source paper "privacy with estimation guarantees"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "generalizing bottleneck problems"
    - "matching anonymized and obfuscated time series to users profiles"
    - "privacy preserving adversarial networks"
    - "renyi differential privacy"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "an estimation theoretic view of privacy"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-7 (induced by source paper "vector gaussian ceo problem under logarithmic loss and applications"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss"
    - "chaining mutual information and tightening generalization bounds"
    - "on the universality of the logistic loss function"
    - "minimax learning for remote prediction"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "universality of logarithmic loss in lossy compression"
    - "a generalization of blahut arimoto algorithm to compute rate distortion regions of multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-8 (induced by source paper "an operational approach to information leakage"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "technical privacy metrics a systematic survey"
    - "measuring secrecy by the probability of a successful guess"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "a large deviations approach to secure lossy compression"
    - "mitigating timing side channel in shared schedulers"
    - "hiding the rumor source"

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)