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: "decision provenance harnessing data flow for accountable systems"
  Abstract: Demand is growing for more accountability regarding the technological systems that increasingly occupy our world. However, the complexity of many of these systems—often systems-of-systems—poses accountability challenges. A key reason for this is because the details and nature of the information flows that interconnect and drive systems, which often occur across technical and organizational boundaries, tend to be invisible or opaque. This paper argues that data provenance methods show much promise as a technical means for increasing the transparency of these interconnected systems. Specifically, given the concerns regarding ever-increasing levels of automated and algorithmic decision-making, and so-called “algorithmic systems” in general, we propose decision provenance as a concept showing much promise.  Decision provenance  entails using provenance methods to provide information exposing decision pipelines: chains of inputs to, the nature of, and the flow-on effects from the decisions and actions taken (at design and run-time) throughout systems. This paper introduces the concept of decision provenance, and takes an interdisciplinary (tech-legal) exploration into its potential for assisting accountability in algorithmic systems. We argue that decision provenance can help facilitate oversight, audit, compliance, risk mitigation, and user empowerment, and we also indicate the implementation considerations and areas for research necessary for realizing its vision. More generally, we make the case that considerations of data flow, and systems more broadly, are important to discussions of accountability, and complement the considerable attention already given to algorithmic specifics.

Connected through hyperedge HE-1 (induced by source paper "trustworthy ai in the age of pervasive computing and big data"):
  with members:
    - "segment integrated gradients better attributions through regions"
    - "model cards for model reporting"
    - "attention based prototypical learning towards interpretable confident and robust deep neural networks"
    - "a machine learning approach to prevent malicious calls over telephony networks"
    - "a style based generator architecture for generative adversarial networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "personalized and private peer to peer machine learning"

Connected through hyperedge HE-2 (induced by source paper "monitoring misuse for accountable artificial intelligence as a service"):
  with members:
    - "artificial intelligence as a services ai aas on software defined infrastructure"
    - "responsible facial recognition and beyond"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "algorithms that remember model inversion attacks and data protection law"
    - "stealing machine learning models via prediction apis"
    - "societal economic ethical and legal challenges of the digital revolution from big data to deep learning artificial intelligence and manipulative technologies"

Connected through hyperedge HE-3 (induced by source paper "the relationship between trust in ai and trustworthy machine learning technologies"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "sok security and privacy in machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "generating adversarial malware examples for black box attacks based on gan"

Connected through hyperedge HE-4 (induced by source paper "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"):
  with members:
    - "about ml annotation and benchmarking on understanding and transparency of machine learning lifecycles"
    - "ores lowering barriers with participatory machine learning in wikipedia"
    - "towards traceability in data ecosystems using a bill of materials model"
    - "measurement and fairness"
    - "datasheets for datasets"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "multilingual twitter sentiment classification the role of human annotators"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "automating dynamic consent decisions for the processing of social media data in health research"):
  with members:
    - "some hci priorities for gdpr compliant machine learning"
    - "contextual consent ethical mining of social media for health research"
    - "instagram photos reveal predictive markers of depression"
    - "early stage influenza detection from twitter"

Connected through hyperedge HE-6 (induced by source paper "towards traceability in data ecosystems using a bill of materials model"):
  with members:
    - "model cards for model reporting"
    - "cloud programming simplified a berkeley view on serverless computing"
    - "datasheets for datasets"
    - "ipfs content addressed versioned p2p file system"

Connected through hyperedge HE-7 (induced by source paper "algorithms that remember model inversion attacks and data protection law"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "third party tracking in the mobile ecosystem"
    - "the unintended consequences of overfitting training data inference attacks"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "knock knock who s there membership inference on aggregate location data"
    - "machine learning models that remember too much"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-8 (induced by source paper "trusted multi party computation and verifiable simulations a scalable blockchain approach"):
  with members:
    - "reshaping the use of digital tools to fight malaria"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "deep reinforcement learning that matters"
    - "novel exploration techniques nets for malaria policy interventions"

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.CY(Computers and Society)