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: "contextual proximity detection in the face of context manipulating adversaries"
  Abstract: Contextual proximity detection (or, co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems. We present a systematic assessment of co-presence detection in the presence of a context-manipulating attacker. First, we show that it is feasible to manipulate, consistently control and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Second, based on these capabilities, we show that an attacker who can manipulate the context gains a significant advantage in defeating context-based co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning techniques: features-fusion and decisions-fusion, and show that both are vulnerable to contextual attacks but the latter approach can be more resistant in some cases.

Connected through hyperedge HE-1 (induced by source paper "privacy enhancing context authentication from location sensitive data"):
  with members:
    - "flexible and robust privacy preserving implicit authentication"
    - "conxsense automated context classification for context aware access control"

Connected through hyperedge HE-2 (induced by source paper "survey and systematization of secure device pairing"):
  with members:
    - "demo of bandana body area network device to device authentication using natural gait"
    - "rapid user transparent and trustworthy device pairing for d2d enabled mobile crowdsourcing"
    - "walk unlock zero interaction authentication protected with multi modal gait biometrics"
    - "challenges in mobile multi device ecosystems"
    - "when theory and reality collide demystifying the effectiveness of ambient sensing for nfc based proximity detection by applying relay attack data"
    - "stash securing transparent authentication schemes using prover side proximity verification"
    - "pitfalls in designing zero effort deauthentication opportunistic human observation attacks"
    - "on covert acoustical mesh networks in air"

Connected through hyperedge HE-3 (induced by source paper "proximity verification based on acoustic room impulse response"):
  with members:
    - "sound proof usable two factor authentication based on ambient sound"
    - "soundloc acoustic method for indoor localization without infrastructure"

Connected through hyperedge HE-4 (induced by source paper "stash securing transparent authentication schemes using prover side proximity verification"):
  with members:
    - "sound proof usable two factor authentication based on ambient sound"
    - "mining users significant driving routes with low power sensors"

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.CR(Cryptography and Security)